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Convex Launch Readiness

ConvexOfficialPublished by Convex

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

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

ATTRIBUTION.md

Attribution

This folder is an unmodified copy of the Agent Skill "convex-launch-readiness", 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-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 or Convex.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download convex-launch-readiness.zip