Agent skill · Data & Analytics

codex-autoresearch

Run or resume a measured improvement loop in a local project. Use for benchmark-driven optimization, qualitative quality-gap research, packet logging, dashboard readouts, recovery, and review-branch finalization backed by autoresearch session files.

TheGreenCedargithub.com/TheGreenCedarGitHub ↗
codexApache-2.0
Install
npx skills add TheGreenCedar/codex-autoresearch --skill codex-autoresearch --agent codex

Same command for any agent — swap --agent for claude-code, cursor, copilot.

Facts
Files in the skill folder: 5
SKILL.md size: 11 KB
Bundled scripts: none
Path: plugins/codex-autoresearch/skills/codex-autoresearch/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 831
Language: TypeScript

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Codex Autoresearch Turn an improvement request into a measured, resumable loop. Report the metric, decision, evidence, next action, and real publication state. Do not replace them with a generic claim that the project is "better." ```text setup -> doctor -> next -> log -> state -> finalize-preview ``` Use this as the only Codex-facing Autoresearch skill. Do not route to retired subskills, slash commands, or MCP surfaces. ## Establish the working truth 1. Identify the repository or child package that owns the work. 2. Run `git status --short --branch`; preserve unrelated changes. 3. When changing Autoresearch itself, use the checkout in this repository: - wrapper root: `node plugins/codex-autoresearch/scripts/autoresearch.mjs ...` - package root: `node scripts/autoresearch.mjs ...` 4. Treat source and installed-plugin behavior as different until their version and built-entrypoint fingerprint match. ## Start or resume For a new session: 1. Get the goal, benchmark, primary metric, direction, correctness checks, editable scope, and any real budget. 2. Use `prompt-plan` or `setup-plan` when one of those is unclear. Both are read-only. 3. Run `setup` only after the contract is clear en

What's inside
Steps it walks through
  1. Establish the working truth
  2. Start or resume
  3. Run one packet at a time
  4. Research broad or qualitative work
  5. Show the dashboard only when it helps
  6. Finalize accepted work
  7. Keep parent ownership clear
  8. Load only the documentation you need
Ships with 4 files
  • agents/openai.yaml
  • references/dashboard-trust.md
  • references/loop-operations.md
  • references/research-finalize.md
Commands it runs
node scripts/autoresearch.mjs config --cwd <project> --commit-paths "<editable-paths>"
node scripts/autoresearch.mjs doctor --cwd <project> --check-benchmark --explain
node scripts/autoresearch.mjs next --cwd <project>
node scripts/autoresearch.mjs log --cwd <project> --from-last --status measure --description "Baseline measurement"
node scripts/autoresearch.mjs state --cwd <project> --report
node scripts/autoresearch.mjs log --cwd <project> --from-last --status keep --description "<what changed>" --asi-json-file <path>
node scripts/autoresearch.mjs research-start --cwd <project> --slug <slug> --goal "<goal>"
node scripts/autoresearch.mjs serve --cwd <project>
npm run check
About this skill
What does the codex-autoresearch skill do?

Run or resume a measured improvement loop in a local project. Use for benchmark-driven optimization, qualitative quality-gap research, packet logging, dashboard readouts, recovery, and review-branch finalization backed by autoresearch session files.

How do I install it?

Run `npx skills add TheGreenCedar/codex-autoresearch --skill codex-autoresearch --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From TheGreenCedar/codex-autoresearch, a repository with 831 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going