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codex-readiness-unit-test

codex-readiness-unit-test

github.com/openaiTesting & quality28.4k61 installsUpdated 18w ago
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About codex-readiness-unit-test

codex-readiness-unit-test is a skill for assessing AGENTS/PLANS documentation with deterministic local checks and in-session LLM evaluation. It generates JSON and HTML reports without external APIs or HTTP calls.

Key features

  1. 01Filesystem-based deterministic checks
  2. 02In-session LLM evaluation
  3. 03Read-only and confirmed execute modes
  4. 04Strict JSON validation and retries
  5. 05JSON and HTML scorecards

Use cases

  1. 01Assessing agent instruction documentation
  2. 02Checking documented build and test loops
  3. 03Reviewing a confirmed plan's execution

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/openai/skills --skill codex-readiness-unit-test

After installing, type/codex-readiness-unit-testin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

LICENSE.txt
references/
commands.md
default.md
execution_explanation.md
json_fix.md
loop_quality.md
project_context.md
references/checks/
checks.json
scripts/
collect_evidence.py
deterministic_rules.py
run_plan.py
scoring.py

Details

From the project README

LLM Codex Readiness Unit Test

Instruction-first, in-session "readiness" for evaluating AGENTS/PLANS documentation quality without any external APIs or SDKs. All checks run against the current working directory (cwd), with no monorepo discovery. Each run writes to .codex-readiness-unit-test/<timestamp>/ and updates .codex-readiness-unit-test/latest.json. Keep execution deterministic (filesystem scanning + local command execution only). All LLM evaluation happens in-session and must output strict JSON via the provided references.

Quick Start

  1. Collect evidence:
    • python skills/codex-readiness-unit-test/bin/collect_evidence.py
  2. Run deterministic checks:
    • python skills/codex-readiness-unit-test/bin/deterministic_rules.py
  3. Run LLM checks using references in references/ and store .codex-readiness-unit-test/<timestamp>/llm_results.json.
  4. If execute mode is requested, build a plan, get confirmation, run:
    • python skills/codex-readiness-unit-test/bin/run_plan.py --plan .codex-readiness-unit-test/<timestamp>/plan.json
  5. Generate the report:
    • python skills/codex-readiness-unit-test/bin/scoring.py --mode read-only|execute

Outputs (per run, under .codex-readiness-unit-test/<timestamp>/):

  • report.json
  • report.html
  • summary.json
  • logs/* (execute mode)

Runbook

This skill produces a deterministic evidence file plus an in-session LLM evaluation, then compiles a JSON report and HTML scorecard. It requires no OpenAI API key and makes no external HTTP calls.

Minimal Inputs
  • mode: read-only or execute (required)
  • soft_timeout_seconds: optional (default 600)
Modes (Read-only vs Execute)
  • Read-only: Collect evidence, run deterministic rules, and run LLM checks #3–#5. No commands are executed, check #6 is marked NOT_RUN, and no execution logs/summary are produced.
  • Execute: Everything in read-only plus a confirmed plan.json is executed via run_plan.py. This enables check #6 and produces execution logs + execution_summary.json for scoring.

Always ask the user which mode to run (read-only vs. execute) before proceeding.

Check Types
  • Deterministic: filesystem-only checks (#1 AGENTS.md exists, #2 PLANS.md exists, #3 AGENTS.md <= 300 lines, #4 config.toml exists at repo root, repo .codex/, or user .codex/)
  • LLM: in-session Codex evaluation (#3 project context, #4 commands, #5 loops; commands may live in AGENTS or referenced skills)
  • Hybrid: deterministic execution + LLM rationale (#6 execution)

Skill references are discovered from AGENTS.md via $SkillName or .codex/skills/<name> patterns; their SKILL.md files are added to evidence for the LLM checks.

All checks run relative to the current working directory and are defined in skills/codex-readiness-unit-test/references/checks/checks.json, weighted equally by default. Each run writes outputs to .codex-readiness-unit-test/<timestamp>/ and updates .codex-readiness-unit-test/latest.json. The helper scripts read .codex-readiness-unit-test/latest.json by default to locate the latest run directory.

Strict JSON + Retry Loop (Required)

For each LLM/HYBRID check:

  1. Run the specialized prompt expecting strict JSON.
  2. If JSON is invalid or missing keys, run skills/codex-readiness-unit-test/references/json_fix.md with the raw output.
  3. Retry up to 2 additional attempts (max 3 total).
  4. If still invalid: mark the check as WARN with rationale: "Invalid JSON from evaluator after retries".

The JSON schema is:

{
  "status": "PASS|WARN|FAIL|NOT_RUN",
  "rationale": "string",
  "evidence_quotes": [{"path":"...","quote":"..."}],
  "recommendations": ["..."],
  "confidence": 0.0
}
Single Confirmation (Required)

Combine the command summary and execute plan into one concise confirmation step. Present:

  • The extracted build/test/dev loop commands (human-readable, labeled).
  • The planned execute details (cwd, ordered commands, soft timeout policy, env). Ask for a single confirmation to proceed. Do not paste raw JSON, full evidence, or the full plan.json. If declined, mark execute-required checks as NOT_RUN.
Required Files
  • .codex-readiness-unit-test/<timestamp>/evidence.json (from collect_evidence.py)
  • .codex-readiness-unit-test/<timestamp>/deterministic_results.json (from deterministic_rules.py)
  • .codex-readiness-unit-test/<timestamp>/llm_results.json (from in-session references)
  • .codex-readiness-unit-test/<timestamp>/execution_summary.json (execute mode only)
  • .codex-readiness-unit-test/<timestamp>/report.json and .codex-readiness-unit-test/<timestamp>/report.html (from scoring.py)
  • .codex-readiness-unit-test/<timestamp>/summary.json (structured pass/fail summary from scoring.py)
  • .codex-readiness-unit-test/latest.json (stable pointer to the latest run directory)
Prompt Mapping
  • #3 project_context_specified → skills/codex-readiness-unit-test/references/project_context.md
  • #4 build_test_commands_exist → skills/codex-readiness-unit-test/references/commands.md
  • #5 dev_build_test_loops_documented → skills/codex-readiness-unit-test/references/loop_quality.md
  • #6 dev_build_test_loop_execution → skills/codex-readiness-unit-test/references/execution_explanation.md
plan.json schema (execute mode)
{
  "project_dir": "relative/or/absolute/path (optional)",
  "cwd": "optional/absolute/path (defaults to current directory)",
  "commands": [
    {"label": "setup", "cmd": "npm install"},
    {"label": "build", "cmd": "npm run build"},
    {"label": "test", "cmd": "npm test"}
  ],
  "env": {
    "EXAMPLE": "value"
  }
}

Place plan.json inside the run directory (e.g., .codex-readiness-unit-test/<timestamp>/plan.json).

llm_results.json schema
{
  "project_context_specified": {"status":"PASS","rationale":"...","evidence_quotes":[],"recommendations":[],"confidence":0.7},
  "build_test_commands_exist": {"status":"PASS","rationale":"...","evidence_quotes":[],"recommendations":[],"confidence":0.7},
  "dev_build_test_loops_documented": {"status":"WARN","rationale":"...","evidence_quotes":[],"recommendations":[],"confidence":0.6},
  "dev_build_test_loop_execution": {"status":"PASS","rationale":"...","evidence_quotes":[],"recommendations":[],"confidence":0.6}
}
Scoring Rules
  • PASS = 100% of weight
  • WARN = 50% of weight
  • FAIL/NOT_RUN = 0%
  • Overall status: FAIL if any FAIL; else WARN if any WARN or NOT_RUN; else PASS.
Safety + Timeouts
  • Denylisted commands are not executed and marked FAIL.
  • Soft timeout defaults to 600s; hard cap defaults to 3x soft timeout.
  • Execution logs are written to .codex-readiness-unit-test/<timestamp>/logs/.

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