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Use Railway

ClineOfficialPublished by Cline

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Operate Railway infrastructure: sign up for or sign in to a Railway account, create projects, provision services and databases, manage object storage buckets, deploy code, configure environments and variables, manage domains, troubleshoot failures, check status and metrics, set up Railway agent tooling, and query Railway docs. Use this skill whenever the user mentions Railway, signing up, creating an account, registering, logging in, deployments, services, environments, buckets, object storage, build failures, agent setup, MCP, or infrastructure operations, even if they don't say "Railway" explicitly. Also invoke this skill when the user asks to be signed up, registered, or onboarded to Railway: do not refuse — drive them through the unauthed `railway up` flow (deploys + signs up on the fly) or `railway login` (which creates new accounts on the fly).
— the description by Cline

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The use-railway skill provides a comprehensive interface for operating Railway infrastructure. It handles the full lifecycle of applications and services, from initial account setup and project creation to deployment, environment management, and troubleshooting. The skill intelligently routes requests between the Railway CLI, Remote MCP, and local CLI MCP based on the specific operational context, ensuring that tasks like deploying code, managing object storage, or analyzing database performance are executed using the most appropriate and reliable method.

What it does

  • Provisions and manages Railway projects, services, and environments.
  • Handles code deployments and manages service configuration and variables.
  • Facilitates account sign-up, sign-in, and authentication flows.
  • Provides tools for troubleshooting, including log retrieval and metric analysis.
  • Manages S3-compatible object storage buckets and domain configurations.
  • Configures Railway agent tooling and integrates with MCP for platform operations.

When to use it

  • When you need to deploy an application or service to Railway.
  • When setting up a new Railway project or managing existing infrastructure.
  • When you need to troubleshoot build failures, service crashes, or performance issues.
  • When configuring environment variables, domains, or storage buckets.
  • When you need to perform database introspection or analysis on Railway-hosted databases.
  • When you need to authenticate or onboard a user to the Railway platform.

Advantages

  • Automates complex infrastructure workflows like multi-step deployments.
  • Intelligently selects the best tool path (CLI vs. MCP) for the specific task.
  • Simplifies authentication by handling sign-up and sign-in flows automatically.
  • Provides structured, reliable output using JSON for programmatic analysis.
  • Reduces manual overhead by managing context and state across Railway resources.

Usage examples

“Deploy this directory to Railway.”
The agent runs 'railway up' to automatically sign the user in if needed, create the project and service, and deploy the application.
“Check the status of my production service.”
The agent uses the appropriate Railway MCP or CLI tool to fetch and display the current deployment status, logs, and metrics.
“I need to add a PostgreSQL database to my project.”
The agent uses the Railway CLI to provision the database and returns the connection credentials.
“Why is my service crashing?”
The agent retrieves recent logs and metrics for the service, analyzes them, and reports the likely cause of the failure.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/cline/use-railway/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip use-railway.zip -d .agents/skills

Documentation by Cline

Use Railway

Railway resource model

Railway organizes infrastructure in a hierarchy:

  • Workspace is the billing and team scope. A user belongs to one or more workspaces.

  • Project is a collection of services under one workspace. It maps to one deployable unit of work.

  • Environment is an isolated configuration plane inside a project (for example, production, staging). Each environment has its own variables, config, and deployment history.

  • Service is a single deployable unit inside a project. It can be an app from a repo, a Docker image, or a managed database.

  • Bucket is an S3-compatible object storage resource inside a project. Buckets are created at the project level and deployed to environments. Each bucket has credentials (endpoint, access key, secret key) for S3-compatible access.

  • Deployment is a point-in-time release of a service in an environment. It has build logs, runtime logs, and a status lifecycle.

Most CLI commands operate on the linked project/environment/service context. Use railway status --json to see the context, and --project, --environment, --service flags to override.

Tool routing

Railway has three agent-facing operation paths. Choose the path that matches the job:

  • Remote MCP (https://mcp.railway.com): account/project/service discovery, deployment state, bounded logs, simple redeploys, simple project creation, or complex Railway workflows that can be handed to railway-agent. Remote MCP uses Railway OAuth and does not depend on local CLI state.

  • Local CLI MCP (railway mcp): CLI-backed platform operations such as variables, domains, service config, templates, metrics, HTTP summaries, buckets, volumes, docs, or deploy-from-directory.

  • Railway CLI (railway): workflows that depend on local machine state such as current working directory deploys, railway up, railway run, SSH, database analysis scripts, local linking, interactive setup, or exact command output.

If multiple paths are available, choose the one that preserves the needed context. Remote MCP fits OAuth-scoped platform operations that do not need local files or CLI state. Local CLI MCP or the CLI fit workflows that need the current repo, local credentials, SSH, database scripts, or commands not exposed by remote MCP.

Use scripts/railway-api.sh only when neither MCP nor CLI exposes the operation, or when a reference gives a specific GraphQL fallback.

Parsing Railway URLs

Users often paste Railway dashboard URLs. Extract IDs before doing anything else:

https://railway.com/project/<PROJECT_ID>/service/<SERVICE_ID>?environmentId=<ENV_ID>
https://railway.com/project/<PROJECT_ID>/service/<SERVICE_ID>

The URL always contains projectId and serviceId. It may contain environmentId as a query parameter. If the environment ID is missing and the user specifies an environment by name (e.g., "production"), resolve it:

scripts/railway-api.sh \
  'query getProject($id: String!) {
    project(id: $id) {
      environments { edges { node { id name } } }
    }
  }' \
  '{"id": "<PROJECT_ID>"}'

Match the environment name (case-insensitive) to get the environmentId.

Prefer passing explicit IDs to CLI commands (--project, --environment, --service) and scripts (--project-id, --environment-id, --service-id) instead of running railway link. This avoids modifying global state and is faster.

Intent-based routing

Route by user intent before running preflight checks. The preflight ceremony below is for diagnostic and configuration work — it adds friction when the user just wants to ship something or sign up.

Deploy-from-cwd intent ("deploy", "ship", "push to Railway", "deploy this app"):

  • Skip the railway whoami / railway status preflights.

  • Run railway up directly — it self-validates auth, signs the user in (the CLI opens a browser) if they're unauthenticated, and chains into project + service creation and deploy.

  • Announce intent before invoking: "Running railway up — it'll sign you in if needed and deploy this directory."

  • Do NOT ask the user to run railway login first. The chain handles auth as part of the deploy.

  • If the environment can't open a browser, the CLI prints a device-code sign-in link and waits — follow Device-code sign-in: relay the link immediately (run in background, relay the link to the user the moment it prints).

Signup intent ("sign me up", "create my Railway account", "register me", "get me on Railway"):

  • If the current directory has a deployable app (e.g. package.json, requirements.txt, go.mod, Dockerfile, source to build), run railway up — it signs the user up and deploys in one shot, landing them on a running app. A detected agent harness authorizes the project creation, so bare railway up is enough — there's no extra prompt to clear. Use it even when the user only said "sign me up": shipping their app is the goal, so don't make them pick a command and don't drop to a bare login. For scripted or agent runs, railway up -y is the robust form — it skips prompts and forces the create non-interactively even if harness detection misses. railway login is NOT the default for signup when there's something to deploy.

  • Only when there is nothing to deploy — an empty / non-app directory, or the user explicitly says they just want an account with no deploy — use railway login (creates new accounts on the fly through the same OAuth surface). There is no separate signup command.

  • Signup is the flow most likely to hit the device-code wait (brand-new users in sandboxed/headless agent environments). Follow Device-code sign-in: relay the link immediately — a signup lost to an expired code is a lost user, not a retry.

Sandbox / remote-build intent ("give me a sandbox", "spin up a scratch environment", "build this remotely", "run this remotely"):

  • Load sandbox.md and follow it. Sandboxes require the feature to be enabled in Priority Boarding — if a sandbox command fails with a feature-availability error, prompt the user to enable Sandboxes in Priority Boarding rather than retrying.

Other intents (querying state, listing projects, configuring variables, debugging failures):

  • Follow the Preflight section below.

Preflight

Before any mutation, verify the tool path and context:

command -v railway                # CLI installed
RAILWAY_CALLER="skill:[email protected]" RAILWAY_AGENT_SESSION="railway-skill-$(date +%s)-$$" railway whoami --json
railway --version                 # check CLI version

Exception: railway up and railway login self-validate auth and run their own unauth-aware flows. Don't run railway whoami before them — it adds a redundant failing call without changing what you do next. See Account creation & sign-in.

Skill freshness

Once per session, alongside the preflight checks, verify the installed Railway agent tooling is current. Root help ends with an Agent tooling: health section (printed to stderr, CLI 5.9+; older CLIs print nothing — skip this check rather than retrying):

railway --help 2>&1 | grep -A4 "Agent tooling:"

Act on what it reports:

  • Skills installed and up to date — continue; nothing to do.

  • An update is available (rev <a> → <b> available) — run railway skills update, then tell the user to restart their coding tool so the updated skills load. This session keeps running on the old skill revision until restart: finish the current request, and if this skill's guidance disagrees with the updated CLI's own output, trust the CLI.

  • Skills or MCP server missing (✗ lines) — run railway setup agent -y, then tell the user to restart their coding tool.

Check once per session and don't re-run it after acting; the restart prompt to the user is the resolution, not another check.

When Railway MCP is available and the job is a platform-state read, use the matching MCP read instead of shelling out. If using the CLI path, run the CLI checks above.

For Railway CLI calls made while this skill is active, prefix the command with RAILWAY_CALLER=skill:[email protected] and a stable RAILWAY_AGENT_SESSION reused for the current user request. Generate the session id once per user request, then reuse that exact value for later Railway CLI calls in the same workflow. Do not run a separate export preflight solely for telemetry; inline env prefixes keep the shell output concise and avoid leaking setup steps into every response.

Context resolution - URL IDs always win:

  • If the user provides a Railway URL, extract IDs from it. Do NOT run railway status --json; it returns the locally linked project, which is usually unrelated.

  • If no URL is given, fall back to railway status --json for the linked project/environment/service.

  • When using MCP tools after resolving local context with railway status --json, pass the resolved project, environment, and service IDs explicitly. Do not rely on MCP implicit linked context; MCP may not share the CLI's current working directory link.

If the CLI is missing, guide the user to install it.

bash <(curl -fsSL https://railway.com/install.sh) --agents -y # Install CLI and configure detected agents
bash <(curl -fsSL https://railway.com/install.sh) # Shell script (macOS, Linux, Windows via WSL)
npm i -g @railway/cli # npm (macOS, Linux, Windows). Requires Node.js version 16 or higher.
brew install railway # Homebrew (macOS)

If not authenticated, see Account creation & sign-in below — the CLI offers unauthed railway up (deploy + sign up/in in one shot) or railway login (sign up/in only; new accounts created on the fly). If not linked and no URL was provided, run railway link --project <id-or-name>.

If a command is not recognized (for example, railway environment edit), the CLI may be outdated. Upgrade with:

railway upgrade

Account creation & sign-in

Railway uses a single unified OAuth flow for both sign-in and sign-up. The backend detects fresh accounts from durable compliance state (a CLI client that hasn't accepted ToS / Fair Use yet) and adapts the consent screen and post-auth landing page — new users land on a "Welcome to Railway!" page, existing users see the standard confirmation. The CLI does not declare signup intent up front.

Two commands surface this flow, depending on intent:

CommandWhen to use
railway upAgent-friendly onboarding from the current directory. Unauthenticated → opens the browser (or device-code) to sign in / sign up. With no linked project, a detected agent harness (or -y) auto-creates a project + service and deploys; an interactive human is offered create / link-existing / cancel. Add -y to skip prompts and force the create non-interactively (works even if harness detection misses).
railway loginSign in — and sign up. New accounts are created on the fly through the same OAuth surface; there is no separate signup command.

Related: railway up --new creates a fresh project + service from the current directory and deploys it even if one is already linked (use when already signed in and the user wants a new app); --name <name> overrides the project name.

Choosing the path:

  • Deploy from cwd → run railway up (interactive) or railway up -y (skips the confirm prompt). Run it yourself; don't ask the user to sign in separately first.

  • New project from cwd when already signed in → railway up --new.

  • Sign up with a deployable app in cwd → railway up (signs up and deploys — bare up works for a detected agent, even if the user only said "sign me up"; add -y to skip prompts / force it non-interactively). Sign in, or sign up with nothing to deploy → railway login (creates new accounts on the fly).

Headless / no browser:

The CLI auto-detects SSH sessions, CI, and a missing DISPLAY and switches to the device-code flow on its own — you almost never need to force it.

Do NOT pass --browserless just because you are an agent or your shell is non-interactive. If the human is at this machine (a local IDE or desktop session — the common case), bare railway login opens their browser directly, which completes far more reliably than relaying a device code (~90% vs ~60% success for agent-driven sign-ins). Being a coding agent does not make the machine headless.

railway login --browserless   # ONLY for machines with genuinely no browser

Forces the device-code flow (RFC 8628): prints a sign-in link and a short code for the user to open on any device. Reserve it for machines where no browser exists — SSH boxes, containers, remote VMs the auto-detection missed. When you do end up in a device-code flow, follow the relay procedure below: surface the sign-in link to the user the moment it prints.

Agent harness, human present: when the CLI detects an agent harness with a human at the keyboard, railway up opens the browser and skips the confirm prompt — the agent invocation is treated as consent. A real human still has to complete OAuth in the browser.

Device-code sign-in: relay the link immediately (CRITICAL):

When the CLI can't open a browser (sandboxed shell, container, SSH, no DISPLAY), unauthed railway up and railway login print a sign-in URL + short code and then block, polling for up to 10 minutes while the user completes sign-in. The code expires after 10 minutes. If you run this as a normal foreground command, your harness buffers the output until the command exits — the user never sees the link until the code is already dead. This is the #1 cause of failed agent-driven signups. Handle it like this:

  1. Preferred — background execution (when the agent environment supports background commands and output polling):

    • Start the command in the background.

    • Poll its output. The instant a sign-in block appears (Sign in with one click: <url> on newer CLIs, or Sign in at: <url> / Enter this code: <code> on older ones), stop everything and relay it to the user verbatim — do not summarize, shorten, or defer it. Prefer the one-click URL when present; otherwise relay the URL and code together. Tell the user to open the link now.

    • Leave the command running and keep polling. When the user completes sign-in, the same process picks up the session and continues into the deploy on its own. Then verify per the deploy rules below.

  2. No background support — set expectations, use the longest timeout:

    • Before running, tell the user: "This will print a sign-in link — I'll show it to you the moment I have it. Please complete it promptly; the code expires in 10 minutes."

    • Run with the longest timeout your harness allows.

    • If the command times out or is killed before sign-in completed, the printed code is no longer being monitored — a late click does nothing. Relay whatever link appeared anyway for context, then immediately re-run the command and relay the new link, telling the user to always use the newest one.

  3. Never wait silently for the command to finish before showing the link, and never report the sign-in as failed without first relaying the link and giving the user a chance to act.

The browser transport needs none of this — the CLI opens the browser on the user's machine itself.

JSON / CI modes do not auto-prompt: railway up --json and railway up --ci will NOT open a browser for an unauthed user. --json emits a structured error instead:

{"error":"Not signed in.","code":"NOT_AUTHENTICATED","hint":"Run `railway login` to authenticate, then re-run."}

When you see code: NOT_AUTHENTICATED, authenticate the user with railway login, then retry the original command.

Fully unattended (no human at all): set RAILWAY_API_TOKEN (account-scoped) or RAILWAY_TOKEN (project-scoped) instead of running an interactive login. A brand-new user with no token and no human present cannot complete signup — there is no headless account-creation path.

Agent tooling

Use direct Railway CLI commands for deterministic operations. Use railway agent only when the user explicitly asks for Railway Agent, wants a natural-language investigation, or the task is broader than a single resource operation.

Set up Railway skills, MCP, and authentication with:

railway setup agent
railway setup agent -y
railway setup agent --remote

railway setup agent -y skips the interactive login flow. If the user isn't authenticated after setup, run railway login.

Install or update MCP and skills directly when the user names a target tool:

railway mcp install
railway mcp install --agent codex
railway mcp install --agent cursor --remote
railway skills
railway skills update --agent codex
railway skills remove --agent cursor

Supported targets include claude-code, cursor, codex, opencode, copilot, and factory-droid. The --remote flag configures https://mcp.railway.com instead of a local railway mcp stdio server.

Use Railway Agent chat with:

railway agent
railway agent -p "why is my service crashing?"
railway agent -p "summarize the deployment status" --json
railway agent --list --json
railway agent --thread-id <thread-id>

railway agent requires user OAuth authentication from railway login. Project tokens (RAILWAY_TOKEN) are not supported for Railway Agent chat. If an agent command is unavailable, upgrade with railway upgrade --yes.

Common quick operations

These are frequent enough to handle without loading a reference. Use the matching MCP tool when the job is platform-scoped and the tool is available; otherwise use the CLI:

railway status --json                                    # current context
railway whoami --json                                    # auth and workspace info
railway project list --json                              # list projects
railway service list --json                              # services in current environment (verify before retrying `add`)
railway add --database <type> --json                     # add one database; ALWAYS pass --json
railway add --service <name> --json                      # add empty service; ALWAYS pass --json
railway variable list --service <svc> --json             # list variables
railway variable set KEY=value --service <svc>           # set a variable
railway logs --service <svc> --lines 200 --json          # recent logs
railway metrics --service <svc> --since 1h --json        # resource and HTTP metrics summary
railway up --detach -m "<summary>"                       # deploy current directory (returns at QUEUED — verify before reporting)
railway deployment list --json                           # poll newest deployment status after a detached up
railway bucket list --json                               # list buckets in current environment
railway bucket info --bucket <name> --json               # bucket storage and object count
railway bucket credentials --bucket <name> --json        # S3-compatible credentials

Routing

For anything beyond quick operations, load the reference that matches the user's intent. Load only what you need, one reference is usually enough, two at most.

IntentReferenceUse for
Analyze a database ("analyze <url>", "analyze db", "analyze database", "analyze service", "introspect", "check my postgres/redis/mysql/mongo")analyze-db.mdDatabase introspection and performance analysis. analyze-db.md directs you to the DB-specific reference. This takes priority over the status/operate routes when a Railway URL to a database service is provided alongside "analyze".
Create or connect resourcessetup.mdProjects, services, databases, buckets, templates, workspaces
Ship code or manage releasesdeploy.mdDeploy, redeploy, restart, build config, monorepo, Dockerfile
Change configurationconfigure.mdEnvironments, variables, config patches, domains, networking
Check health or debug failuresoperate.mdStatus, logs, metrics, build/runtime triage, recovery
Use a sandbox or build remotely ("sandbox", "scratch environment", "ephemeral box", "build remotely", "remote build", "run this remotely")sandbox.mdCreate/fork sandboxes, run commands remotely, remote template builds, port forwarding, teardown. Requires Sandboxes enabled in Priority Boarding — if unavailable, prompt the user to enable it.
Request from API, docs, or communityrequest.mdRailway GraphQL API queries/mutations, metrics queries, Central Station, official docs

If the request spans two areas (for example, "deploy and then check if it's healthy"), load both references and compose one response.

Execution rules

  1. Use Railway MCP for platform operations that match an available MCP tool.

  2. Use the local CLI for workflows that need the current repo, local shell, SSH, database scripts, or unsupported MCP coverage.

  3. Fall back to scripts/railway-api.sh for operations neither MCP nor CLI exposes.

  4. Use --json output where available for reliable parsing.

  5. Resolve context before mutation. Know which project, environment, and service you're acting on.

  6. For destructive actions (delete service, remove deployment, drop database), confirm intent and state impact before executing.

  7. After mutations, verify the result with a read-back command or MCP read.

  8. Never report a deploy as successful without observing a terminal SUCCESS. railway up --detach returning (it prints "Build queued") and a streaming railway up cut off by a shell timeout only confirm the build started. Poll railway deployment list --json until the newest deployment's status is SUCCESS (report deployed), or FAILED/CRASHED (triage per operate.md — do not claim success). A streaming up that exits on its own is authoritative: exit 0 = deployed, exit 1 = failed.

User-only commands (NEVER execute directly)

These commands modify database state and require the user to run them directly in their terminal. Do NOT execute these with Bash. Instead, show the command and ask the user to run it.

CommandWhy user-only
python3 scripts/enable-pg-stats.py --service <name>Modifies shared_preload_libraries, may restart database
python3 scripts/pg-extensions.py --service <name> install <ext>Installs database extension
python3 scripts/pg-extensions.py --service <name> uninstall <ext>Removes database extension
ALTER SYSTEM SET ...Changes PostgreSQL configuration
DROP EXTENSION ...Removes database extension
CREATE EXTENSION ...Installs database extension

When these operations are needed:

  1. Explain what the command does and any side effects (e.g., restart required)

  2. Show the exact command the user must run

  3. Wait for user confirmation that they ran it

  4. Verify the result with a read-only query

Composition patterns

Multi-step workflows follow natural chains:

  • Add object storage: setup (create bucket), setup (get credentials), configure (set S3 variables on app service)

  • First deploy: setup (create project + service), configure (set variables and source), deploy, operate (verify healthy)

  • Fix a failure: operate (triage logs), configure (fix config/variables), deploy (redeploy), operate (verify recovery)

  • Add a domain: configure (add domain + set port), operate (verify DNS and service health)

  • Docs to action: request (fetch docs answer), route to the relevant operational reference

When composing, return one unified response covering all steps. Don't ask the user to invoke each step separately.

Setup decision flow

When the user wants to create or deploy something, determine the right action from current context:

  1. If the intent is deploy-from-cwd or signup-from-cwd, skip railway whoami and run railway up (or railway up -y) directly per Intent-based routing — it handles signup, project creation, service creation, and deploy in one chain. For other setup flows that need workspace/account context first, run railway whoami --json; if it fails with an auth error the user has no token — route through Account creation & sign-in.

  2. Run railway status --json in the current directory.

  3. If linked: add a service to the existing project (railway add --service <name>). Do not create a new project unless the user explicitly says "new project" or "separate project".

  4. If not linked: check the parent directory (cd .. && railway status --json).

    • Parent linked: this is likely a monorepo sub-app. Add a service and set rootDirectory to the sub-app path.

    • Parent not linked: run railway list --json and look for a project matching the directory name.

      • Match found: link to it (railway link --project <name>).

      • No match: create a new project (railway init --name <name>).

  5. When multiple workspaces exist, match by name from railway whoami --json.

Naming heuristic: app names like "flappy-bird" or "my-api" are service names, not project names. Use the directory or repo name for the project.

Response format

For all operational responses, return:

  1. What was done (action and scope).

  2. The result (IDs, status, key output).

  3. What to do next (or confirmation that the task is complete).

Keep output concise. Include command evidence only when it helps the user understand what happened.

Files

references/analyze-db-postgres.md

PostgreSQL Analysis

This reference covers PostgreSQL-specific metrics, tuning, and analysis guidance. For common analysis patterns (output structure, collection status handling, performance thinking), see analyze-db.md.

What the Script Collects

collection_status — check this FIRST. Shows what succeeded vs failed:

  • database_query: SSH → psql batched query (connections, cache, vacuum, queries, etc.)

  • metrics_api: Railway API for disk, CPU, memory

  • logs_api: Railway API for recent log lines

  • ha_cluster: SSH → Patroni REST API (HA services only)

Each entry has "status" ("success", "error", or "skipped") and optional "error" or "reason" fields.

All in ONE operation (no additional queries needed):

Connections:

  • Current/max/available counts

  • States (active, idle, idle_in_transaction)

  • By application name

  • By age (buckets: <1min, 1-5min, 5-60min, 1-24hr, >24hr)

  • Oldest connection age

Memory & Configuration:

  • shared_buffers, effective_cache_size, work_mem, maintenance_work_mem

  • WAL settings, parallelism settings, planner settings

  • Autovacuum status

  • track_activity_query_size (tells you if queries are truncated in pg_stat_statements)

  • log_min_duration_statement (tells you if slow query logging is enabled and at what threshold)

  • idle_in_transaction_session_timeout, statement_timeout (safety timeouts)

  • track_io_timing (needed for blk_read_time/blk_write_time in query stats)

Cache Performance:

  • Overall table/index hit ratios

  • Per-table: hit %, disk reads, size (this is key for diagnosis)

Storage:

  • Database size, WAL size

  • Per-table: total size, data size, index size, row count

Vacuum Health:

  • Per-table: dead rows, dead %, vacuum count, last vacuum/analyze, XID age

  • Flags: needs_vacuum, needs_freeze

Indexes:

  • Unused indexes (0 scans) with sizes

  • Invalid indexes (failed builds)

Query Performance (if pg_stat_statements enabled):

  • Top 100 queries by total execution time

  • Per-query execution: calls, total_min, mean_ms, min_ms, max_ms, stddev_ms

  • Per-query rows: total rows, rows_per_call

  • Per-query planning: total_plan_ms, mean_plan_ms

  • Per-query cache: shared_blks_hit, shared_blks_read, shared_blks_dirtied, shared_blks_written, cache_hit_pct

  • Per-query temp: temp_blks_read, temp_blks_written

  • Per-query I/O timing: blk_read_time_ms, blk_write_time_ms (requires track_io_timing=on)

  • Per-query WAL: wal_records, wal_bytes

  • Per-query local blocks: local_blks_hit, local_blks_read (for temp tables)

  • Temp file stats (cumulative since stats reset, NOT current disk usage)

Logs & Active Issues:

  • recent_logs: Raw unfiltered logs (1000 lines) - parse these yourself, look for errors, warnings, patterns

  • recent_errors: Filtered error-level logs (legacy, for quick reference)

  • long_running_queries: Queries running >5s at time of collection

  • blocked_queries: Queries waiting on locks

  • cluster_logs: HA cluster events (Patroni)

Important: Always analyze the raw recent_logs array thoroughly. This is 1000 lines of unfiltered database output — treat it as a goldmine.

Log analysis checklist — go through ALL of these:

  1. Error/Fatal/Panic messages: Count them, categorize them, quote the exact messages

    • ERROR: deadlock detected → cross-reference with deadlock count in database_stats

    • FATAL: too many connections → cross-reference with connection usage

    • ERROR: canceling statement due to statement timeout → which queries are timing out?

    • FATAL: out of shared memory → shared_buffers or lock table exhaustion

    • ERROR: could not extend file → disk space issue

    • PANIC: ... → database crash, investigate immediately

  2. Slow query log entries (if log_min_duration_statement is set):

    • Count how many slow queries appear

    • Identify which tables/queries are mentioned most often

    • Cross-reference with top_queries — the same patterns should appear in both

    • Note the actual durations logged vs mean_ms from pg_stat_statements

  3. Autovacuum activity:

    • LOG: automatic vacuum of table → is autovacuum running? How often?

    • LOG: automatic analyze of table → statistics being updated

    • WARNING: oldest xmin is far in the past → XID wraparound risk

    • Absence of autovacuum entries with high dead rows → autovacuum may be blocked or misconfigured

  4. Checkpoint activity:

    • LOG: checkpoint starting / LOG: checkpoint complete → how frequent?

    • checkpoint complete: wrote X buffers (Y%) → high Y% means lots of dirty data

    • Time between checkpoints — if < 5 minutes, write load is high

    • checkpoints are occurring too frequently → increase max_wal_size

  5. Connection patterns:

    • LOG: connection received / LOG: connection authorized → connection rate

    • LOG: disconnection → normal or unexpected? Check session duration

    • FATAL: remaining connection slots are reserved → max_connections hit

    • FATAL: password authentication failed → unauthorized access attempts

  6. Replication messages:

    • LOG: started streaming WAL → replica connected

    • ERROR: requested WAL segment has already been removed → replica too far behind

    • FATAL: could not receive data from WAL stream → replication broken

  7. Temporal patterns:

    • Are errors clustered in time? (burst vs steady)

    • Do slow queries correlate with checkpoint times?

    • Is there a pattern suggesting cron jobs or batch processing?

State what you found with specifics: "Analyzed 1000 log lines covering 2024-01-15 14:00 to 15:30. Found: 23 slow query warnings (all SELECT on UserSession table, 200-800ms), 4 autovacuum runs, 2 checkpoints (normal interval), 0 errors. The slow queries correlate with the UserSession table's 76% cache hit rate."

Log Interpretation When Only Logs Are Available

When collection_status.database_query failed and you only have logs:

Startup vs steady-state logs:

  • LOG: database system is ready to accept connections — normal startup, NOT evidence of a crash

  • LOG: started streaming WAL — normal replication, NOT an error

  • LOG: checkpoint starting / LOG: checkpoint complete — routine operation

  • FATAL: the database system is starting up — transient during restarts, NOT a persistent problem

What you CAN say from logs alone:

  • Whether errors or warnings are present and their frequency

  • Whether the database recently restarted (and that this is normal during deploys)

  • Whether there are connection refused errors (possible saturation or startup)

What you CANNOT say from logs alone:

  • Whether the database is performing well or poorly

  • Whether cache hit ratios are good

  • Whether vacuum is behind

  • Whether queries are slow

  • Any tuning recommendations

If only logs are available, explicitly state: "No performance conclusions possible — database metrics were not collected."

Active Issues:

  • Long-running queries (>5s)

  • Idle in transaction (>30s)

  • Blocked queries (waiting on locks)

  • Lock contention details

Infrastructure (7d + 24h) — show both windows so trends can be compared:

7-Day Trends

MetricCurrentAvgMinMaxTrend
CPU0.02 vCPU0.020.000.18stable
Memory320 MB290 MB240 MB380 MBstable
Disk4.2 GB4.1 GB3.9 GB4.3 GBincreasing (+8%)

Last 24 Hours

MetricCurrentAvgMinMaxTrend
CPU0.04 vCPU0.020.000.18stable
Memory320 MB295 MB270 MB340 MBstable
Disk4.2 GB4.15 GB4.1 GB4.2 GBstable

Compare: "Disk growing slowly over 7d but stable over 24h → gradual data growth, not an acute event."

Do NOT show cpu_limit/memory_limit columns or utilization %. Railway auto-scales — these limits are just the ceiling. See analyze-db.md autoscale rules.

Replication / HA (if applicable):

  • Replication status

  • HA cluster status (Patroni)

  • Background writer stats

  • WAL archiver status

PostgreSQL Tuning Knowledge

Use this to reason about configuration issues:

Memory Parameters
ParameterDefaultTargetWhat It Does
shared_buffers128MB25% RAMThe database's main cache. Pages read from disk go here. Too small = constant disk I/O.
effective_cache_size4GB75% RAMNOT memory allocation - a hint to the planner about OS cache. Too low = planner avoids indexes.
work_mem4MB16-64MBMemory per sort/hash/join operation. Too low = temp files on disk. Caution: multiplied by concurrent operations.
maintenance_work_mem64MB256MB-1GBMemory for VACUUM, CREATE INDEX. Higher = faster maintenance.
Tuning Formulas
shared_buffers = RAM × 0.25 (max 40%)
  1GB RAM  → 256MB
  4GB RAM  → 1GB
  16GB RAM → 4GB

work_mem = (RAM / max_connections) / 4
  4GB RAM, 100 conns → 10MB
  8GB RAM, 200 conns → 10MB

effective_cache_size = RAM × 0.75
  4GB RAM  → 3GB
  16GB RAM → 12GB
Settings Requiring Restart vs Immediate

Restart required:

  • shared_buffers

  • max_connections

  • max_parallel_workers

Immediate (SIGHUP):

  • work_mem

  • effective_cache_size

  • random_page_cost

  • checkpoint_completion_target

SSD vs HDD

Railway uses SSDs. If random_page_cost = 4.0 (HDD default), the planner thinks random reads are 4x more expensive than sequential - it avoids index scans. Set to 1.1-2.0 for SSDs.

Railway auto-scales vertically

See analyze-db.md for full autoscale rules. For PostgreSQL specifically:

  • Tune parameters relative to the current RAM from metrics_history.memory.current, not memory_limit.

  • If shared_buffers is undersized relative to current RAM, recommend increasing it to 25% of current RAM.

  • If the working set far exceeds what 25% of current RAM can hold, note this as a limitation of the current memory footprint — but do NOT tell the user to increase RAM. The platform handles that automatically.

Thresholds for Reasoning

MetricHealthyWarningCritical
Cache hit ratio>99%95-99%<95%
Per-table cache hit>95%80-95%<80% with high reads
Connection usage<70%70-90%>90%
Disk usage<70%70-85%>85%
Dead rows %<5%5-20%>20%
XID age<100M100-150M>150M (emergency at 2B)
Vacuum Priority Matrix

Dead row percentage alone doesn't determine urgency. Use this matrix:

Table SizeDead RowsPriority
> 100 MB> 10,000High - real bloat affecting performance
> 50 MB> 5,000Medium - worth addressing
< 10 MBAnyLow - negligible impact, ignore
Any< 1,000Low - autovacuum will handle it

A 1 MB table with 25% dead rows has ~250 KB of bloat. Not worth mentioning as "critical".

Applying Fixes

When recommending changes, include the actual SQL and always explain side effects — especially for settings that add overhead or change behavior.

-- Memory tuning (example for 4GB RAM)
ALTER SYSTEM SET shared_buffers = '1GB';
ALTER SYSTEM SET effective_cache_size = '3GB';
ALTER SYSTEM SET work_mem = '32MB';
ALTER SYSTEM SET random_page_cost = 1.5;
SELECT pg_reload_conf();
-- Note: shared_buffers requires restart
-- Vacuum specific tables
VACUUM ANALYZE "TableName";

-- Emergency XID freeze
VACUUM FREEZE "TableName";
Side effects to document per setting
SettingSide Effect to Explain
track_io_timingAdds a system call (gettimeofday) per block read/write. On most modern systems the overhead is <1%, but on systems with slow clock sources it can be measurable. Worth it for the diagnostic value in pg_stat_statements (blk_read_time, blk_write_time).
shared_buffersRequires restart. Allocates memory at startup — over-allocating can starve OS cache and other processes.
work_memMultiplied by concurrent operations (sorts, hashes, joins). 64MB × 50 concurrent ops = 3.2 GB. Recommend conservatively.
log_min_duration_statementLogging slow queries adds I/O. A threshold too low (e.g., 100ms) on a high-throughput DB can generate massive log volume. Start at 1000ms.
idle_in_transaction_session_timeout / statement_timeoutWill kill queries/transactions that exceed the timeout. Existing application code that relies on long-running transactions or queries will break. Warn the user to verify their application can handle this.

Enabling pg_stat_statements

ONLY suggest this if BOTH conditions are true:

  1. pg_stat_statements_installed is false in the JSON output

  2. top_queries is empty or missing

If these conditions are met, tell the user to run (do NOT execute with Bash):

python3 scripts/enable-pg-stats.py --service <name>

This may require a brief restart.

If pg_stat_statements_installed: true and top_queries has data, DO NOT suggest enabling it.


PostgreSQL-Specific Guidance

The sections below apply specifically to PostgreSQL analysis via scripts/analyze-postgres.py.

How to Think About PostgreSQL Performance

The Core Question

When you see a problem, ask: What is the chain of causation?

Example chain:

  1. Cache hit is 89% (symptom)

  2. Email table has 6% cache hit with 1.19B disk reads (deeper symptom)

  3. Email table is 1.7GB, shared_buffers is 128MB (root cause)

  4. The table is 13x larger than the buffer pool - it will NEVER fit in cache

  5. Every query touching Email forces disk I/O

This reasoning is what you provide. The script gives you the data points - you connect them.

Patterns to Look For

Memory Starvation Pattern:

  • Low cache hit + large tables + small shared_buffers = working set doesn't fit

  • High temp files + low work_mem = sorts/hashes spilling to disk

  • These often occur together - both indicate the database needs more memory

Important: Temp file stats (temp_files, temp_bytes) are cumulative since the last stats reset, not current disk usage. When reporting, say "X GB written to temp files since stats reset" - not "X GB on disk right now".

Vacuum Neglect Pattern:

  • High dead rows % + "never" vacuum timestamps = autovacuum isn't keeping up

  • Multiple tables with >10% dead rows = systemic issue, not one-off

  • High XID age + vacuum issues = potential wraparound emergency

Important: Consider absolute impact, not just percentage. A tiny table (< 10 MB) with 20% dead rows has negligible impact - vacuuming it reclaims almost nothing. Prioritize tables with BOTH high dead row counts (thousands+) AND meaningful size (tens of MB+). Don't mark small tables as "critical" just because of a high percentage.

Missing Index Pattern:

  • High seq_scan count + 0 idx_scans on large tables = queries scanning full tables

  • Low cache hit on specific tables + high seq_scans = indexes would help AND reduce I/O

Connection Pressure Pattern:

  • High connection % + many idle connections = connection pooling needed

  • Old connections (days) + idle_in_transaction = potential connection leaks or stuck transactions

Slow Query Analysis — Go Deep

The top_queries array is the most valuable data for customers. This is where you can give the most actionable, specific advice. Don't skim it — analyze every query in the top 10-15 thoroughly.

Per-Query Fields and What Each Tells You
FieldWhat It MeansHow to Interpret
callsNumber of times this query pattern executedHigh calls × even small mean_ms = huge cumulative impact. A 5ms query called 10M times = 833 minutes of DB time
total_minTotal execution time in minutesThe primary sort key. This is the query's total footprint on the database
mean_msAverage execution time per callCompare with stddev — if stddev >> mean, the query has wildly variable performance
min_ms / max_msFastest and slowest executionA 2ms min with 30,000ms max means the query sometimes hits pathological cases (lock waits, cache misses, bloated tables)
stddev_msStandard deviation of execution timeHigh stddev = unpredictable. The query probably performs well when data is cached but terribly when it's not. This is often the query causing random user-visible latency spikes
rows_per_callAverage rows returned per execution0.01 rows/call means the query usually returns nothing — might be a polling pattern or existence check that could use EXISTS instead. 50,000 rows/call suggests missing pagination or bulk fetch
mean_plan_msAverage planning timeIf plan time is >5ms, the planner is spending significant time. Could indicate: too many partitions, complex joins needing better statistics (ALTER TABLE SET STATISTICS), or pg_catalog bloat
cache_hit_pct% of blocks found in shared_buffers<90% = query is constantly going to disk. Cross-reference with the table it touches in cache_per_table
shared_blks_readBlocks read from disk (not cache)This is the raw I/O cost. Each block = 8KB. 1M blocks read = 8GB of disk I/O
shared_blks_dirtiedBlocks this query modifiedHigh dirtied blocks = write-heavy query. These blocks will need to be flushed to disk during checkpoints
shared_blks_writtenBlocks this query had to flush to disk itselfShould be 0 in a healthy system. >0 means the query was forced to do its own I/O because shared_buffers was full of dirty pages — a sign of severe memory pressure
temp_blks_read / temp_blks_writtenBlocks spilled to temp filesAny nonzero value means the query exceeded work_mem. Each block = 8KB. temp_blks_written of 1M = 8GB spilled to disk for sorts/hashes
blk_read_time_ms / blk_write_time_msTime spent on actual disk I/O (requires track_io_timing)If available and high, this tells you exactly how much time was spent waiting on disk vs CPU. If 0, track_io_timing may be off
wal_records / wal_bytesWAL generated by this queryHigh WAL = write-heavy. If one query generates most WAL, it's driving replication lag and checkpoint pressure
local_blks_hit / local_blks_readBlocks for temporary tablesIf nonzero, query uses temp tables — common in complex CTEs or materialized subqueries
Red Flags — What Demands Explanation
SignalWhat It MeansExampleWhat to Tell the Customer
Low cache_hit_pct (< 90%)Query hitting disk constantlycache_hit_pct: 47.19"This query reads X blocks from disk each call. The table it touches (Y) is Z GB but shared_buffers is only W MB — the data physically cannot stay cached"
High temp_blks (any nonzero)Query spilling sorts/hashes to disktemp_blks_written: 39102928"This query spills ~X GB to temp files per execution because work_mem (Y MB) is too small for its sort/hash. Each spill means disk I/O instead of memory"
Huge rows_per_call (>1000)Missing pagination or bulk fetchrows_per_call: 12177"Each call returns ~12K rows. If this is a user-facing query, it likely needs LIMIT/OFFSET or cursor-based pagination. If it's a batch job, it's expected"
Near-zero rows_per_call with high callsPolling or existence check pattern0.01 rows/call, 500K calls"This query runs 500K times but almost never finds data. If it's checking for new work, consider LISTEN/NOTIFY instead of polling. If it's an existence check, ensure it uses EXISTS with LIMIT 1"
stddev >> meanWildly variable performancemean=15ms, stddev=2400ms, max=45000ms"This query averages 15ms but sometimes takes 45 SECONDS. The high stddev means unpredictable latency. Likely causes: lock contention, cache misses on cold data, or table bloat causing variable scan times"
High mean_plan_ms (>5ms)Expensive query planningmean_plan_ms: 23.4"The planner spends 23ms just deciding HOW to run this query, before executing it. With X calls, that's Y minutes of pure planning overhead. Consider: PREPARE'd statements, simpler joins, or increasing default_statistics_target for better stats"
shared_blks_written > 0Memory pressure forcing query I/Oshared_blks_written: 50000"This query was forced to flush dirty pages to disk itself because shared_buffers was full. This is a sign of severe buffer pool pressure — increase shared_buffers"
High wal_bytes relative to othersWrite-heavy query driving replicationwal_bytes: 5000000000"This query generates X GB of WAL, which is Y% of total WAL. It's the primary driver of replication lag and checkpoint I/O"
max_ms >> 10× mean_msPathological worst casesmean=50ms, max=120000ms"The worst execution was 2400× slower than average. Investigate: was it blocked by a lock? Did it hit a cold cache after restart? Is there table bloat causing some scans to be much longer?"
How to Present Slow Queries

Show the full table first with all available metrics (the report already includes these columns):

| Query (truncated) | Calls | Total (min) | Mean (ms) | Min/Max (ms) | Stddev | Rows/Call | Cache Hit | Temp R/W | Plan (ms) | I/O Time |
|-------------------|-------|-------------|-----------|--------------|--------|-----------|-----------|----------|-----------|----------|
| SELECT Email.ccFull... | 78K | 132 | 101 | 0.3/8200 | 340 | 0.05 | 47% | 0/0 | 1.2 | 45000 |
| SELECT Thread... ORDER BY | 48K | 223 | 279 | 2.1/45000 | 2400 | 12,177 | 98.8% | 0/39M | 0.4 | 800 |
| SELECT Content... | 1.3K | 12 | 563 | 180/3200 | 420 | 0.65 | 1.8% | 0/0 | 8.3 | 31000 |

Then analyze EACH query — this is the most valuable part. For each of the top 10 queries, explain:

  1. What the query does — identify the tables, the pattern (lookup, join, aggregation, pagination)

  2. Why it's slow — connect the specific metrics to a root cause

  3. The cascading impact — how this query affects overall database health

  4. Specific fix — not generic advice, but targeted to what the metrics show

Example deep analysis:

Query 1: Email.ccFull join (78K calls, 101ms mean, 132 min total)

  • Pattern: Joins Email → EmailThreadKind → Thread → EmailEntry. ORM-generated N+1 or bulk join.

  • Root cause: 47% cache hit means 53% of blocks come from disk. The Email table is 1.7GB but shared_buffers is 128MB — only 7.5% of this table can be cached at once. Every call displaces other data from cache, creating a cascading eviction problem.

  • The stddev of 340ms with max of 8200ms means some calls take 80× longer — likely when the needed pages were just evicted by another query.

  • I/O time of 45,000ms total confirms this: the query has spent 45 seconds just waiting for disk across all calls.

  • rows_per_call = 0.05 means it almost never finds a match — it's doing all this I/O for an existence-check pattern. An EXISTS() subquery with proper index could eliminate the full table scan.

  • Fix: (a) Increase shared_buffers to 1GB so the hot portion stays cached. (b) Add index on Email(ccFull, threadId) to avoid the sequential scan. (c) Rewrite as EXISTS if the app only needs presence, not the full row.

Query 2: Thread pagination (48K calls, 279ms mean, 223 min total)

  • Pattern: SELECT Thread... ORDER BY with large result set. Pagination query.

  • Root cause: rows_per_call = 12,177 — returning 12K rows per call is a pagination bug (missing LIMIT) or an admin/batch endpoint.

  • temp_blks_written = 39M (312 GB of temp files!) — the ORDER BY creates a sort that exceeds work_mem (4MB), so it spills to disk every single time.

  • stddev = 2400ms with max = 45,000ms — some executions take 45 seconds, likely when disk temp files compete with other I/O.

  • Cache hit is 98.8% — the data itself is cached, but the sort still spills because work_mem is separate from shared_buffers.

  • Fix: (a) Add LIMIT if this is user-facing. (b) Create an index matching the ORDER BY clause to eliminate the sort entirely. (c) Increase work_mem to 32-64MB so the sort fits in memory.

Truncate Long Queries Intelligently
  • Show the table names and key operations (JOIN, WHERE, ORDER BY)

  • Don't dump 2000-character ORM-generated SQL

  • Identify the pattern: "Thread zone assignment lookup" not the full SQL

  • For ORM queries with $1, $2, ... parameters, note that the actual values aren't available — the pattern matters more than specific values

  • Note on query truncation: pg_stat_statements stores full query text up to track_activity_query_size (default 1024 chars). ORM-generated queries often exceed this — if a query ends abruptly, it was truncated by PostgreSQL, not by our script. The JSON output preserves the full text from pg_stat_statements; only the human-readable text report truncates for display

Query Workload Profile

After analyzing individual queries, summarize the overall workload:

  • Read vs write ratio: Use tup_returned/tup_fetched vs tup_inserted/tup_updated/tup_deleted from database_stats

  • Top 3 time consumers: Which queries dominate total_min? If 3 queries account for 80% of execution time, that's where to focus

  • Cache pressure sources: Which queries have the most shared_blks_read? They're driving cache misses for everything else

  • Temp file culprits: Which specific queries create temp files? Don't say "increase work_mem" generically — say "Query X creates Y GB of temp files per day"

  • WAL generators: If applicable, which queries generate the most WAL bytes? They're driving replication lag

Correlate Across Sections

The script collects many data points. Look for correlations:

If you see...Check also...Because...
Low table cache hitper-table cache rates, table sizes vs shared_buffersOne large table may be thrashing the cache
High temp fileswork_mem value, top queriesSpecific queries may be the culprits
Dead rows building upvacuum health, XID ageAutovacuum may be blocked or misconfigured
Seq scans on large tablesunused indexes, index hit ratesMay have indexes but planner isn't using them
High connection usageconnection age, idle_in_transactionMay be leaks, not actual load
Synthesize Insights the Script Can't

The script flags individual issues. You should:

  1. Identify the PRIMARY bottleneck - What's the #1 thing hurting performance right now?

  2. Explain cascading effects - How does one problem cause others?

  3. Prioritize fixes - What should they do first, second, third?

  4. Warn about risks - What happens if they don't fix this?

Important: Synthesis is prose that EXPLAINS the data tables you already showed. Don't hide data in prose - the tables make it visible, the prose connects the dots.

Example flow:

  1. Show config table: shared_buffers = 128 MB vs recommended 1 GB

  2. Show cache table: Email table at 6% cache hit with 1.19B disk reads

  3. THEN explain: "Your buffer pool (128 MB) is 13x smaller than your Email table (1.7 GB). This single table is dragging down your overall 89% cache hit rate."

The user sees the data, understands the relationship, then gets the explanation. Don't make them trust your conclusions without seeing the evidence first.

Common Errors to Avoid (PostgreSQL-Specific)

  • Saying "enable pg_stat_statements" when pg_stat_statements_installed: true and top_queries has data

  • Misreporting connection usage (check percent field, not just current)

  • Ignoring the oldest_connections details when flagging old connections

  • Saying "746 GB of temp files on disk" when temp_bytes is cumulative since stats reset

  • Marking tiny tables (< 10 MB) as "critical" for vacuum just because of high dead row percentage

  • Listing slow queries by total_time only without analyzing cache_hit_pct, temp_blks, and rows returned

  • Dumping full ORM-generated SQL instead of summarizing the query pattern

Validated against

  • PostgreSQL system views: pg_stat_activity, pg_stat_statements, pg_statio_user_tables, pg_stat_user_tables

  • Patroni REST API for HA clusters

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs an MCP server, Needs network access, Needs a command-line tool, Needs Python, Needs Node.js

Security

What our scanner noticed· Medium risk

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  • Scripts that your agent may run

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Attribution and licence

Published by
Cline
Publisher
Cline (Official)
Source
cline/skills · skills/use-railway · commit 2637846
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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