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 livlign/claude-skills --skill readme-doctorgit clone --depth 1 https://github.com/livlign/claude-skillsWrote 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/livlign/claude-skills/readme-doctor)<a href="https://agentmods.dev/skills/livlign/claude-skills/readme-doctor"><img src="https://agentmods.dev/badge/skills/livlign/claude-skills/readme-doctor/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/livlign/claude-skills/readme-doctor"><img src="https://agentmods.dev/badge/skills/livlign/claude-skills/readme-doctor.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 | $0.00088 | $0.02465 |
| Opus 5.5 | $0.00035 | $0.00986 |
| Sonnet 5.5 | $0.00018 | $0.00493 |
| Haiku 4.5 | $0.00009 | $0.00247 |
Grade A, and why
readme-doctor 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- readme-rubric — 94% identical, 10 lines differ
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
readme-doctor
Most README problems aren't typos or missing sections — they're shape problems: the hero never lands, install-to-first-success buries the lede, the "what is this" sentence assumes prior context, the badge row is louder than the content. This skill audits a README against patterns that consistently correlate with maintainer outcomes (stars, contributor onboarding, issue quality) and produces a punch list a maintainer can act on in an afternoon.
The skill's quality comes from the rubric being grounded in the repo's actual audience and category, not from a generic checklist. A README for a 50K-star framework needs different things than a personal-project utility.
Phases
- Discovery — pick operating mode (Auto / Semi-auto / Manual, §1.1), input the README, infer the repo's category and audience from the scan, agree on what "good" looks like for this specific repo.
- Audit — walk the rubric, score each criterion, gather evidence (cite line numbers).
- Punch list — prioritize findings P0 (blocking) / P1 (high-leverage) / P2 (nice-to-have), each with a one-line fix.
- Optional output — read-only summary by default; on user request, open a PR with concrete edits.
Phase 1 — Discovery
1.1 Operating mode (ask first)
Same three modes as repo-visuals — Auto, Semi-auto (recommended), Manual. Use AskUserQuestion. Mode affects how many decisions are silent vs surfaced; it does not skip rubric checks.
1.2 Input
User may provide:
- GitHub URL →
gh repo view+ clone shallow - Local path → read directly
- Pasted README text → analyze in place, but the rubric loses any check that needs file-tree context (manifest signals, screenshot presence,
examples/dir, etc.). Flag these as "skipped — paste-only mode." - Nothing → ask first
1.3 Scan (collect before judging)
- README full text + rendered length (lines, words, time-to-scan estimate at 250wpm)
- Manifest:
package.json,Cargo.toml,pyproject.toml, etc. — version, description, keywords - File tree (depth 2): does an
examples/exist?docs/? Image assets? - Recent commits (last 10): is the repo active? When was last release?
- GitHub signals: stars, topics, open issues count, latest release date
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 · 194 lines · 88 tokens per session scan A 169e60b69ff2
readme-doctor is a skill published in the GitHub repository livlign/claude-skills (20 stars, last pushed 12d ago), licensed MIT. It adds 88 tokens to every session and 2,465 once invoked, about $0.0004 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.
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