Agent skill

Convex Launch Readiness

by openclaw in openclaw/clawhub

Run every Convex audit (authz, reviewer, advisor, insights) into one scored, deduped readiness report with an ordered fix plan — Lighthouse for your backend.

MITAuto-check passedProduct & Project Management

Install Convex Launch Readiness

skills CLI
$ npx skills add openclaw/clawhub --skill convex-launch-readiness -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install openclaw/clawhub convex-launch-readiness --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/openclaw/clawhub.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/convex-launch-readiness .claude/skills/convex-launch-readiness && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
convex-launch-readiness
GitHub stars
9.5k
Token cost
~1.2k tokens
SKILL.md length
642 words
Files
1
Skills in repo
55
Repo updated
First seen
Licence
MIT

At a glance

Run every Convex audit (authz, reviewer, advisor, insights) into one scored, deduped readiness report with an ordered fix plan — Lighthouse for your backend.

  • Works in 7 steps: GUARD + SCOPE: deploy-guard classifies… → RUN THE PASSES, each emitting findings… → NORMALIZE + DEDUPE: collect all findings… → …
  • Tasks that involve Feature launches and release readiness
  • SKILL.md covers Workflow and Rules
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Convex Launch Readiness is an agent skill from openclaw/clawhub. Run every Convex audit (authz, reviewer, advisor, insights) into one scored, deduped readiness report with an ordered fix plan — Lighthouse for your backend.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Product & Project Management, covering Feature launches and release readiness, Authorization and RBAC and Web performance. The repository describes itself as: Skill + Plugin Registry for OpenClaw. The licence is MIT.

When your agent uses it

  • Tasks that involve Feature launches and release readiness
  • Tasks that involve Authorization and RBAC
  • Tasks that involve Web performance

Example prompts

  • “/convex-launch-readiness”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. GUARD + SCOPE: deploy-guard classifies the target (local-anonymous / dev / preview / prod); announce it. Detect what's assessable — is…
  2. RUN THE PASSES, each emitting findings on the bus
  3. NORMALIZE + DEDUPE: collect all findings into one report. Set each finding's identity field to a normalized function/table key (e.g…
  4. SCORE, auditable: start at 100; subtract per CONFIRMED finding by severity (high −15, med −5, low −1), floor at 0; print the exact formula…
  5. REPORT: the score, then findings ranked by severity, each with its evidence, its locus, and the fixCapability + a one-line fix note. Group…
  6. DISPATCH on request: for each finding the user accepts, invoke its fixCapability (convex-authz, convex-reviewer's fixers, migrate-rehearse…
  7. 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…

What it can do on your machine

Read from SKILL.md and the folder at commit d044664. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Convex Launch Readiness loads about 1.2k tokens when it runs. Until then it costs about 45 tokens; SKILL.md has 642 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~45
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from openclaw/clawhub at commit d044664, republished under its MIT licence (© openclaw). 642 words, ~1,196 tokens.

Download SKILL.mdSave it as .claude/skills/convex-launch-readiness/SKILL.md (or your agent's skills folder).
name
convex-launch-readiness
description
Run every Convex audit (authz, reviewer, advisor, insights) into one scored, deduped readiness report with an ordered fix plan — Lighthouse for your backend.
<!-- 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

  1. 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).
  2. 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.
  3. NORMALIZE + DEDUPE: collect all findings into one report. Set each finding's identity field 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.
  4. 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.
  5. 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).
  6. 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.
  7. 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'.
Show full SKILL.md (157 more words)Show less

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.

© openclaw, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/convex-launch-readiness of openclaw/clawhub.

Open the folder on GitHubat commit d044664

Compare with similar skills

Convex Launch Readiness next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Convex Launch Readiness compared with similar skills
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Convex Launch Readiness this skillopenclaw/clawhub9.5k—~1.2kAutomated safety check: PassMIT
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Azure LighthouseMicrosoftDocs/Agent-Skills777—~1.6kAutomated safety check: PassCC-BY-4.0
Ohdearohdearapp/ohdear-cli141—~1.1kAutomated safety check: PassMIT
Dev Teamaffaan-m/ECC276k1 repos~2.1kAutomated safety check: PassMIT
Production Auditaffaan-m/ECC276k1 repos~1.9kAutomated safety check: PassMIT

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Questions about Convex Launch Readiness

What does Convex Launch Readiness do?

Run every Convex audit (authz, reviewer, advisor, insights) into one scored, deduped readiness report with an ordered fix plan — Lighthouse for your backend. Convex Launch Readiness is an agent skill from openclaw/clawhub. Run every Convex audit (authz, reviewer, advisor, insights) into one scored, deduped readiness report with an ordered fix plan — Lighthouse for your backend.

When should I use Convex Launch Readiness?

Convex Launch Readiness fits situations like: tasks that involve Feature launches and release readiness; tasks that involve Authorization and RBAC; tasks that involve Web performance.

How do I install Convex Launch Readiness in Claude Code?

Run `npx skills add openclaw/clawhub --skill convex-launch-readiness -a claude-code`. Or copy the skill folder (.agents/skills/convex-launch-readiness in openclaw/clawhub) into .claude/skills/convex-launch-readiness in your project. Claude Code loads it when a task matches its description.

How do I install Convex Launch Readiness in Codex?

Run `npx skills add openclaw/clawhub --skill convex-launch-readiness -a codex`. Or copy the skill folder (.agents/skills/convex-launch-readiness in openclaw/clawhub) into .agents/skills/convex-launch-readiness in your project. Codex loads it when a task matches its description.

Can I use Convex Launch Readiness in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add openclaw/clawhub --skill convex-launch-readiness -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/convex-launch-readiness, .gemini/skills/convex-launch-readiness, .github/skills/convex-launch-readiness and .opencode/skills/convex-launch-readiness in your project.

What does Convex Launch Readiness need to run?

SKILL.md names no scripts, command-line tools or credentials: Convex Launch Readiness is instructions for the agent only.

Does Convex Launch Readiness access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Convex Launch Readiness safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Convex Launch Readiness use?

Convex Launch Readiness is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Convex Launch Readiness use?

About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Convex Launch Readiness?

Skills that share tags, products or a category with Convex Launch Readiness: Performance Capacity (majiayu000/spellbook, 287 stars), Azure Lighthouse (MicrosoftDocs/Agent-Skills, 777 stars), Ohdear (ohdearapp/ohdear-cli, 141 stars) and Dev Team (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Convex Launch Readiness?

openclaw (a GitHub organization) maintains it in openclaw/clawhub, which has 9,500 GitHub stars. The repository holds 55 skills in this directory. The repository was last updated on October 8, 2026.

Source: openclaw/clawhub on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.