Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Atman36/codex-maintainer-kit --skill pr-factory-scoutgit clone --depth 1 https://github.com/Atman36/codex-maintainer-kitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/atman36/codex-maintainer-kit/pr-factory-scout)<a href="https://agentmods.dev/skills/atman36/codex-maintainer-kit/pr-factory-scout"><img src="https://agentmods.dev/badge/skills/atman36/codex-maintainer-kit/pr-factory-scout.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00108 | $0.01178 |
| Opus 5 | $0.00054 | $0.00589 |
| Sonnet 5 | $0.00022 | $0.00236 |
| Haiku 4.5 | $0.00011 | $0.00118 |
Grade A, and why
pr-factory-scout scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role: Scout
Quick repository assessment and mergeable candidate discovery.
Inputs
{{REPO_ROOT}}- Local repository path{{REPO_URL}}- Repository URL{{BASE_BRANCH}}- Target branch (e.g., "main", "master")
Goal
Quickly decide whether this repo is a good target right now, and list 3–7 mergeable improvement candidates.
Process
- Read documentation: README, CONTRIBUTING, CODE_OF_CONDUCT, LICENSE
- Identify tooling: How to run tests, lint, build (if present)
- Also map detected config paths (
eslint,prettier,pytest.ini,tox.ini,tsconfig, CI workflows, etc.)
- Also map detected config paths (
- Check repo signals: CI config exists? tests folder? recent commits? (local analysis only, don't browse web)
- Generate candidates: 3-7 low-risk improvements with title, type, estimated LOC, likely files, risk, verification plan
For detailed guidance on repo signals and scoring heuristics, see references/repo-signals.md.
Rules
- Be stack-agnostic: Infer conventions from the repo; don't assume any framework
- Prefer small, low-risk PRs: docs/tests/bugfix/CI/DX. Avoid "refactor everything"
- Respect existing tooling: Follow CONTRIBUTING.md and existing tools. Don't suggest adding new deps unless unavoidable
- Schema-valid JSON payload: Build payload conforming to
ExecutionResult - Persist analysis JSON: Write payload to
{{ARTIFACT_DIR}}/scout-<timestamp>.json - Chat output format: Return only
SAVED_JSON_PATH=<absolute_path_to_json> - Put all findings under
data: Keep structured results in the data field
Output Format
- Build JSON conforming to
../../schemas/execution_result.schema.json:
{
"schema_version": "1.0",
"id": "scout-<timestamp>",
"stage": "scout",
"status": "success",
"summary": "Found 5 low-risk candidates in active repo with CI/tests",
"started_at": "2024-01-15T10:30:00Z",
"finished_at": "2024-01-15T10:32:00Z",
"exit_code": 0,
"artifacts": [],
"metrics": {
"duration_ms": 120000,
"cost_usd": 0.05,
"tokens_in": 15000,
"tokens_out": 3000
},
"errors": [],
"warnings": [],
"data": {
"repo_profile": {
"stack_hints": ["typescript", "react", "jest"],
"ci_detected": ["github-actions"],
"commands": {
"test": "npm test",
"lint": "npm run lint",
"build": "npm run build"
},
"config_paths": {
"test": ["pytest.ini"],
"lint": [".eslintrc.json"],
"build": ["package.json"],
"ci": [".github/workflows/ci.yml"]
},
"constraints_from_contributing": [
"Run tests before submitting PR",
"Follow conventional commits format"
]
},
"candidates": [
{
"id": "cand-1",
"title": "Add missing test for edge case in parseURL",
"change_type": "test",
"risk": "low",
"est_loc": 15,
"likely_paths": ["src/utils/parseURL.test.ts"],
"rationale": "Function has 80% coverage, missing edge case for malformed URLs",
"test_plan": ["npm test", "Check coverage report"]
}
],
"repo_score": {
"score_0_10": 8,
"reasons": [
"Active maintenance (commits in last week)",
"CI/CD present",
"Test suite exists",
"Clear contributing guidelines"
],
"blockers": []
}
}
}
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 138 lines · 108 tokens per session scan A b12fdd4cdbde
pr-factory-scout is a skill published in the GitHub repository Atman36/codex-maintainer-kit (2 stars, last pushed 3mo ago), licensed MIT. It adds 108 tokens to every session and 1,178 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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