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Convex Launch Readiness

ConvexOfficialPublished by Convex

  • Skill
  • Workflow
  • Software development
  • Apache-2.0
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From the author

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

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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/convex/convex-launch-readiness/download
  2. Unzip 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

  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'.

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

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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

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Security

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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)
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