Borrowing it
Nothing to install: this file belongs to zernie/vigiles. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zernie/vigiles/main/.claude/skills/pr-to-lint-rule/SKILL.mdgit clone --depth 1 https://github.com/zernie/vigilesWrote 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/zernie/vigiles/pr-to-lint-rule)<a href="https://agentmods.dev/skills/zernie/vigiles/pr-to-lint-rule"><img src="https://agentmods.dev/badge/skills/zernie/vigiles/pr-to-lint-rule/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/zernie/vigiles/pr-to-lint-rule"><img src="https://agentmods.dev/badge/skills/zernie/vigiles/pr-to-lint-rule.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 155 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00038 | $0.02234 |
| Opus 5 | $0.00019 | $0.01117 |
| Sonnet 5 | $0.00008 | $0.00447 |
| Haiku 4.5 | $0.00004 | $0.00223 |
Grade A, and why
pr-to-lint-rule 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 11d 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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Turn a prose rule that no off-the-shelf linter rule matches into a custom
lint rule — the opt-in synthesis step. This is the hand-off target of the
custom rule (⚙) lane in a vigiles audit rule map: audit maps a rule there
when it looks enforceable but nothing off-the-shelf fits. You invoke this skill
explicitly; nothing here runs on its own, and installing vigiles never starts
synthesizing anything.
The point of this skill is not "write a checker." A model can write a plausible-looking checker in seconds. The point is the discipline that makes the result trustworthy: synthesize the rule and an independent test that encodes the rule's real intent, run the checker against adversarial cases it didn't author, and abstain — hand it back as prose — if it leaks. A checker that matches a rule's surface but not its intent gives false confidence (the measured failure mode: most naively-synthesized checkers silently leak). Shipping a green check nobody should trust is worse than shipping nothing.
Arguments
$ARGUMENTS — a prose rule to enforce. Either a custom rule (⚙) line copied
from a vigiles audit report, or free text. Examples:
- "we keep telling people not to import directly from antd — use our design-system barrel"
- "people forget to use our custom logger instead of console.log"
- "wrap outbound API calls in our
withRetryhelper" - "route handlers must go through the
withAuthwrapper"
The pipeline
prose rule
→ detect language + linter (ask if ambiguous)
→ REUSE first: does an off-the-shelf rule already fit? → use it, STOP (no synthesis)
→ clarify intent + read the codebase (what exactly is the violation? the fix?)
→ SYNTHESIZE: the rule + an INDEPENDENT intent-encoding test (adversarial cases)
→ TRUST GATE: run the checker on that test; precision AND recall must be 1.0
pass → KEPT (safe to enforce; wire it in)
leak → ABSTAIN (hand back as prose; never ship a checker it can't prove sound)
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.
- 11d ago First seen · 178 lines · 38 tokens per session scan A 30824d23127e
pr-to-lint-rule is a skill published in the GitHub repository zernie/vigiles (15 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 2,234 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
crit
Review code changes, a plan, a live page (running dev server), or a local HTML file with Crit inline comments and structured human feedback. Use only when the user explicitly invokes /crit or directly asks to use Crit; a generic review request does not count.
crit-story
Author a crit story and continue the interactive review loop only when the user explicitly invokes crit-story or directly asks you to generate a crit story. Do not infer this skill from generic review, PR, or diff-review requests.
cleanup-audit
Audit codebase for dead code, unused exports, orphaned files, and stale manifests.
link-check
Verify @file references in AIWG skills and agents against the linking contract — per-file or corpus-wide, with optional auto-fix.
audit-agents-skills
Audit Claude Code agents, skills, and commands for quality and production readiness. Use when evaluating skill quality, checking production readiness scores, or comparing agents against best-practice templates.
design-patterns
Detect, suggest, and evaluate GoF design patterns in TypeScript/JavaScript codebases. Use when refactoring code, applying singleton/factory/observer/strategy patterns, reviewing pattern quality, or finding stack-native alternatives for React, Angular, NestJS, and Vue.