Convex Launch Readiness
ConvexOfficialPublished by Convex
- Skill
- Workflow
- Software development
- Apache-2.0
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill acts as a central orchestrator for backend health, aggregating findings from multiple audit capabilities into a single, unified report. It normalizes data from code-level reviews and live deployment insights, deduplicating issues to ensure accurate scoring. By providing a transparent, reproducible score and an ordered punch list of fixes, it transforms disparate audit results into a clear, actionable roadmap for production readiness.
What it does
- Orchestrates multiple audit passes including authz, reviewer, advisor, and insights.
- Normalizes and deduplicates findings across code and deployment loci.
- Calculates an auditable, reproducible readiness score based on confirmed findings.
- Generates a prioritized fix plan categorized by severity and impact.
- Supports iterative improvement by re-running audits to show score deltas after fixes.
- Provides explicit reporting on coverage, clearly distinguishing between code-only and production-verified results.
When to use it
- Before deploying a new backend version to production.
- When performing a comprehensive health check on an existing deployment.
- To prioritize technical debt and security improvements in a structured manner.
- When you need a clear, evidence-based assessment of backend readiness.
Advantages
- Reduces noise by collapsing duplicate findings from different audit sources.
- Provides a transparent scoring formula that avoids subjective assessments.
- Ensures logical fix ordering, prioritizing critical issues like data loss and authz.
- Maintains accountability by requiring evidence for score-impacting findings.
- Facilitates a clear feedback loop between identifying issues and verifying fixes.
Usage examples
- “Run a launch readiness report for my current project.”
- The agent executes all available audit passes, aggregates the findings, prints a readiness score, and provides an ordered list of recommended fixes.
- “I have applied the suggested fixes; re-run the readiness audit.”
- The agent re-runs the relevant audit passes, updates the readiness score, and displays the delta to confirm the improvements.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/convex/convex-launch-readiness/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip convex-launch-readiness.zip -d .agents/skills
Documentation by Convex
<!-- GENERATED from convex-agents content/capabilities/launch-readiness.json — do not edit by hand. -->
Launch-readiness report
Readiness is not one check — it's the union of the checks, deduped, ranked, and scored. This capability is pure composition over the findings bus (specs/finding.schema.json): it runs each audit capability, normalizes their outputs into one report (specs/finding-report.schema.json), computes an auditable score, and — because every finding names a fixCapability — hands the user a prioritized, actionable punch list instead of four separate reports. It fixes nothing itself; it decides WHAT to fix and in what order, then dispatches to the fixers.
Workflow
GUARD + SCOPE: deploy-guard classifies the target (local-anonymous / dev / preview / prod); announce it. Detect what's assessable — is there a convex/ dir, a deployed deployment with traffic, an auth foundation? Skip passes whose preconditions aren't met and SAY which were skipped (a skipped pass is not a pass).
RUN THE PASSES, each emitting findings on the bus:
convex-authz — the authz scan (identity-from-arg, missing ownership, PII leak, parent-ref-on-write). Always runnable on code.
convex-reviewer — validators, indexes-not-filter, idiom, error handling. Always runnable on code.
convex-advisor — live read-limit / OCC evidence (only if a deployment with traffic exists; else record 'skipped: no traffic').
convex-insights — recent failures from logs (only if a deployment exists). Run independent passes concurrently; each returns findings, not fixes.
NORMALIZE + DEDUPE: collect all findings into one report. Set each finding's
identityfield to a normalized function/table key (e.g.messages:list) that is the SAME whether the pass reported a code-locus or a deployment-locus for that function — so the SAME defect seen from two loci (reviewer flags a missing index at code-locus, advisor flags its read-limit symptom at deployment-locus) collapses to ONE via the bus's (class, identity) dedup and isn't double-counted in the score. Keep the higher-confidence source. Drop nothing silently; a pass that errored/was skipped is a stated coverage gap, not a clean result.SCORE, auditable: start at 100; subtract per CONFIRMED finding by severity (high −15, med −5, low −1), floor at 0; print the exact formula and the per-class breakdown so the number is reproducible, not a vibe. plausible-only findings are listed as candidates but do NOT move the score (evidence-not-vibes). A deployment/traffic-less run reports a code-only score and says so.
REPORT: the score, then findings ranked by severity, each with its evidence, its locus, and the fixCapability + a one-line fix note. Group by 'blockers' (high) / 'should-fix' (med) / 'nice-to-have' (low). End with the ordered fix plan: which capability to run next, in what order (authz/data-loss first, then perf/scale, then idiom/observability).
DISPATCH on request: for each finding the user accepts, invoke its fixCapability (convex-authz, convex-reviewer's fixers, migrate-rehearse for schema changes, suggest for component swaps). After fixes, RE-RUN the affected passes and show the score delta — the readiness number is only meaningful if it moves when you fix things.
Never claim more coverage than was run: the report header lists which passes ran, which were skipped and why. A green score on a code-only run is 'code looks ready', not 'production-verified'.
Rules
Compose, don't re-implement: run the existing audit capabilities and aggregate their bus findings — never re-derive an authz or perf check inline.
The score counts CONFIRMED findings only, by severity, with the formula printed; plausible findings are candidates that don't move the number.
Normalize each finding's locus to a function/table identity before dedup (map deployment functionId ↔ code file:line) so one defect seen from two loci collapses to one and isn't double-scored; keep the higher-confidence source; drop nothing silently.
Every finding carries its fixCapability; the report ends with an ORDERED fix plan (data-loss/authz first, then scale, then idiom/observability).
Re-run affected passes after fixes and show the score delta — a readiness number that doesn't move when you fix things is theater.
Never claim more than was run: header lists ran/skipped passes; a code-only run yields a code-only score, explicitly labeled.
This is a read + aggregate + dispatch pass; fixes happen in the fixer capabilities, gated by their own consent/deploy-target rules.
Files
- ATTRIBUTION.mdAdded by AutoGineer870 B
- LICENSELicence file added by AutoGineer10.7 KiB
- SKILL.md4.7 KiB
ATTRIBUTION.md
Attribution
This folder is an unmodified copy of the Agent Skill "convex-launch-readiness", distributed by AutoGineer (https://autogineer.com/agent-skills).
Commit: f60629d7c6c1f27eb58a56260546d2445049274d
Published by: Convex (https://github.com/convex)
Publisher: Convex (https://github.com/convex)
Licence: Apache License 2.0 (https://github.com/convex/agent-skills/blob/f60629d7c6c1f27eb58a56260546d2445049274d/LICENSE)
Licence evidence: repository licence LICENSE
Retrieved: 2026-11-07
The skill's own files are unmodified copies. AutoGineer is not affiliated with or endorsed by the author or the publisher.
Files added by AutoGineer
ATTRIBUTION.md: this file, written by AutoGineer
LICENSE: copied from LICENSE
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.
Security
No issues found by our static scan· No issues found
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Attribution and licence
- Published by
- Convex
- Publisher
- Convex (Official)
- Source
- convex/agent-skills · skills/convex-launch-readiness · commit f60629d
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Convex.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download convex-launch-readiness.zip