Generated banner for Convex Self Heal
Generated logo for Convex Self Heal

Convex Self Heal

ConvexOfficialPublished by Convex

  • Skill
  • Workflow
  • Software development
  • Apache-2.0
  • No issues found

From the author

Production error → triaged, root-caused, repaired, and certified (tsc + rehearsal + reproduce-then-gone) fix PR for a human to merge — then confirm the error stops recurring. Never auto-merges.
— the description by Convex

AutoGineer summary

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

The convex-self-heal skill provides a structured, multi-stage workflow for managing production errors. It integrates error capture, root-cause analysis, and automated repair generation, culminating in a certified pull request for human approval. By leveraging preview deployments and real-world data snapshots, the system ensures that proposed fixes are validated against actual invariants and regression tests before they reach the developer, maintaining a strict human-in-the-loop boundary for production deployments.

What it does

  • Triages production errors to distinguish between transient issues, configuration errors, and code defects.
  • Performs automated root-cause analysis using diagnostic tools to identify the source of failures.
  • Generates code repairs on isolated branches rather than modifying production directly.
  • Certifies fixes by running type checks, schema rehearsals on production snapshots, and regression tests.
  • Prepares a comprehensive pull request containing the fix, certification evidence, and audit logs.
  • Monitors post-deployment logs to confirm the error signature has been successfully resolved.

When to use it

  • When you need to automate the investigation and remediation of recurring production errors.
  • When you want to ensure that all proposed fixes are verified against production-like data before human review.
  • When you require a secure, auditable process for applying code or schema changes to resolve system defects.
  • When you want to reduce the manual overhead of triaging and reproducing production bugs.

Advantages

  • Maintains a strict human-in-the-loop boundary by never auto-merging or auto-deploying.
  • Reduces risk by certifying fixes against real-world data invariants before they are proposed.
  • Provides clear auditability through logged actions and evidence-backed pull requests.
  • Prevents the proposal of unverified or uncertain fixes by stopping when root causes are unclear.

Usage examples

“Analyze the latest production errors and propose a fix for the identified schema defect.”
The agent will triage the error, perform root-cause analysis, generate a fix on a branch, certify it against a production snapshot, and open a PR with the evidence.
“Check if the recent production error is still recurring after the latest deployment.”
The agent will inspect the sentinel table and logs to confirm the error signature is gone, or reopen the issue if it persists.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/convex/convex-self-heal/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip convex-self-heal.zip -d .agents/skills

Documentation by Convex

<!-- GENERATED from convex-agents content/capabilities/self-heal.json — do not edit by hand. -->

Gated production self-healing loop

Sentry/Datadog/Vercel can go error→investigate→draft-PR, but they treat the backend as opaque and stop at the human merge gate with an unverified diff. Convex can do the step they can't: because the error rows live in the user's own deployment and the fix can be rehearsed on a preview of that deployment, the platform certifies the fix against real invariants before anyone reviews it. This capability is the composition capstone — it wires sentinel (capture) → the findings bus (diagnose) → the fixers (repair) → migrate-rehearse/tsc/probe (certify) → a human PR (decide) → deploy-guard (promote). The human keeps the merge button; the machine does everything up to and including proving the fix works.

Workflow

  1. GUARD: deploy-guard — this loop reads prod and PROPOSES prod changes; classify + announce the deployment and get the standing consent for the loop's scope up front (what classes of fix it may auto-prepare vs must always defer). Never auto-merge; the human merge is the fixed boundary.

  2. CAPTURE: require sentinel (prod errors in the user's own deployment, redacted at write time). If absent, offer to install it and stop — there is nothing to heal without capture.

  3. TRIAGE a new/ recurring error: pull it via the official MCP (data/run-once-query over the sentinel table, or the monitor's prod_error event). Classify: transient (retry/ignore — do NOT open a PR for a one-off network blip), config (env/secret — hand to env, never guess a secret), or a code/schema defect (proceed).

  4. ROOT-CAUSE on the findings bus: run the relevant audit pass on the implicated function — convex-insights (the failing requests + stacks), convex-advisor (if it's a read-limit/OCC cause), convex-reviewer/convex-authz (if it's a logic/authz defect). Produce a bus finding with evidence (the stack + the reproducing input) and a fixCapability. If root cause is unclear, STOP and report — a wrong fix is worse than an open error.

  5. REPAIR via the finding's fixCapability (convex-authz, reviewer fixers, convex-expert for perf) on a branch — never on prod directly.

  6. CERTIFY against the backend's own invariants BEFORE proposing (this is the differentiator — do not skip any that apply): (a) tsc --noEmit clean; (b) if the fix touches schema/data, run it through migrate-rehearse on a preview seeded with a prod snapshot — the schema-conformance gate must pass on real-shaped data; (c) reproduce-then-confirm-gone: replay the error's triggering input against the fixed code (a convex-test case or an MCP run on the preview) and assert the failure no longer occurs; (d) no-regression: the finding must be gone AND no new bus finding introduced on the touched function. A fix that fails any applicable certification is NOT proposed — it's reported as 'attempted, could not certify' with what failed.

  7. PROPOSE, never merge: open a PR (or a diff for review) containing the fix, the certification evidence (tsc result, rehearsal outcome, the reproduced-then-gone assertion), the original error + finding, and the reversibility note. Label the change class. The human reviews and merges.

  8. PROMOTE on merge via deploy-guard's prod consent; after deploy, re-check the sentinel table + logs (failures) to confirm that error signature stops recurring (do NOT use insights for this — it tracks only OCC/read-limit perf events, not arbitrary error signatures) — the loop is only closed when the error stops recurring in prod. If it recurs, reopen with the new evidence.

  9. BOUND it: only classes the user pre-approved in step 1 are auto-prepared (default-safe set: validator fixes, missing-index adds, ownership-check adds, non-destructive backfills); anything destructive, security-sensitive beyond an added check, or ambiguous is always deferred to explicit human direction. Log every action to an append-only record so the loop is auditable.

Rules

  • The human keeps the merge button — this loop prepares and certifies fixes, it NEVER auto-merges or auto-deploys to prod (matches the industry boundary: no credible system ships unattended prod auto-merge).

  • Certify before proposing: tsc + (schema→migrate-rehearse on a prod-snapshot preview) + reproduce-then-confirm-the-failure-is-gone + no new bus finding. An uncertified fix is reported as 'could not certify', never proposed as done.

  • Triage first: transient blips get retried/ignored, config errors go to env (never guess a secret), only real code/schema defects enter the repair loop.

  • Repair on a branch/preview, never on prod directly; promote only through deploy-guard's fresh prod consent.

  • Only pre-approved fix classes are auto-prepared (default-safe: validator/index/ownership/non-destructive backfill); destructive or ambiguous changes are always deferred to the human.

  • Close the loop for real: after merge+deploy, confirm the error signature stops recurring via the sentinel table + logs (not insights, which only sees perf events); reopen if it persists.

  • Every action is logged to an append-only, auditable record; data residency stays in the user's own deployment (sentinel discipline).

  • If root cause is unclear, STOP and report — an uncertain fix is worse than an open, visible error.

Files

ATTRIBUTION.md

Attribution

This folder is an unmodified copy of the Agent Skill "convex-self-heal", distributed by AutoGineer (https://autogineer.com/agent-skills).

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

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Convex
Publisher
Convex (Official)
Source
convex/agent-skills · skills/convex-self-heal · 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-self-heal.zip