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 statsclaw/statsclaw --skill attributiongit clone --depth 1 https://github.com/statsclaw/statsclawWrote 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/statsclaw/statsclaw/attribution)<a href="https://agentmods.dev/skills/statsclaw/statsclaw/attribution"><img src="https://agentmods.dev/badge/skills/statsclaw/statsclaw/attribution/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/statsclaw/statsclaw/attribution"><img src="https://agentmods.dev/badge/skills/statsclaw/statsclaw/attribution.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00017 | $0.00495 |
| Opus 5.5 | $0.00007 | $0.00198 |
| Sonnet 5.5 | $0.00003 | $0.00099 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
attribution 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 1mo 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.
What it actually says
Shared Skill: Attribution — Commit Authorship Policy
StatsClaw commits are attributed to the user alone. Shipper MUST NOT append any co-author trailer, tooling footer, or bot attribution to commit messages.
Policy
Every commit produced by shipper — in the target repo, the workspace repo, or any brain-seedbank fork — uses a clean message with no trailers of the following forms:
Co-authored-by: StatsClaw <[email protected]>Co-authored-by: Claude <[email protected]>(or any otherCo-authored-by:line)Generated with Claude Codefooterhttps://claude.ai/code/session_...URL- Any other tool-attribution line
The git author and committer are the user, as determined by their local user.name / user.email config. Shipper does NOT pass --author, set GIT_AUTHOR_* / GIT_COMMITTER_*, or configure a bot identity.
Rationale
- The user is the only person responsible for the change.
- GitHub's contributor list is driven by commit author/committer +
Co-authored-bytrailers. Adding bot trailers pollutes the contributor graph (e.g.,StatsClaw-Shipper,Claude). - Tooling footers leak session identifiers and add noise to
git log.
Scope
- Applies to: every commit shipper creates — target repo, workspace repo, brain-seedbank fork.
- Applies to: every commit any other agent makes locally inside its worktree (builder, scriber, simulator) — these commits are squashed/merged back, but their messages should also be clean.
- Does not apply to: PR titles or bodies, issue comments, or any non-commit artifact (those may reference StatsClaw or the workflow when useful).
Enforcement
If shipper (or any other agent) emits a commit with a forbidden trailer, reviewer flags it as a STOP. The fix is to amend or reword the commit before pushing.
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.
- 1mo ago First seen · 46 lines · 17 tokens per session scan A f1b671157eff
attribution is a skill published in the GitHub repository statsclaw/statsclaw (89 stars, last pushed 14d ago), licensed MIT. It adds 17 tokens to every session and 495 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
commit
Commit the current work — runs the quality, consistency and passport gates, branches off main if needed, stages specific files, and writes a commit whose subject states what is now true. Pushes and opens a pull request only with --pr or when the user asks; never merges — a merge happens only when the user explicitly…
issues
Keeps a project's to-do list and memory in GitHub issues — files each finding (from a review, an audit, or the user) as its own issue without duplicates, lists the open issues that bear on the current work, and closes an issue with a comment recording what was wrong, what was tried, what worked, and how it was fixed.…
dev-git-workflow
Git workflow para projetos de software complexo — branch strategy, conventional commits, PR templates, merge flows e regras de autoridade.
git
This skill should be used when the user asks to "commit changes", "create a pull request", "rebase safely", or performs any git/GitHub operation.
seo-drift
Monitoramento de drift de SEO — captura baseline dos elementos críticos, detecta mudanças e rastreia regressões ao longo do tempo ('git para SEO'). Use ao pedir 'SEO drift', 'baseline', 'track changes', 'quebrou alguma coisa', 'SEO regression', 'antes e depois' ou 'deployment check'.
brain-github-export
Export accumulated brain knowledge as CONVENTIONS.md and open a GitHub PR so your whole team can review what the AI team learned.