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Convex Self Heal

OrganizationPopular
openclaw
convex-self-heal

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.

Overview

Publisheropenclaw
Repositoryclawhub
Skill nameconvex-self-heal
Stars
9.5K
Forks
1.5K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by openclaw on GitHub. Read the source before you install it.

Installation

Install the Convex Self Heal AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/openclaw/clawhub/tree/main/.agents/skills/convex-self-heal
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/openclaw/clawhub.git /tmp/clawhub
mkdir -p .claude/skills
cp -r /tmp/clawhub/.agents/skills/convex-self-heal .claude/skills/convex-self-heal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Convex Self Heal in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Convex Self Heal on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Convex Self Heal is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Frequently asked questions

What does the Convex Self Heal AI skill do?

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.

Why use Convex Self Heal on TypingMind?

Because you install it once and use it with any model. Convex Self Heal is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Convex Self Heal in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openclaw/clawhub/tree/main/.agents/skills/convex-self-heal. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Convex Self Heal?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Convex Self Heal?

As many as you like. As long as a model supports skills, you can use Convex Self Heal with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Convex Self Heal AI skill free?

Yes. It is published on GitHub by openclaw under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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