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/audience-check/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/audience-check)<a href="https://agentmods.dev/skills/zernie/vigiles/audience-check"><img src="https://agentmods.dev/badge/skills/zernie/vigiles/audience-check/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/audience-check"><img src="https://agentmods.dev/badge/skills/zernie/vigiles/audience-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 36 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 | $0.00034 | $0.01172 |
| Opus 5 | $0.00017 | $0.00586 |
| Sonnet 5 | $0.00007 | $0.00234 |
| Haiku 4.5 | $0.00003 | $0.00117 |
Grade A, and why
audience-check 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 9d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Re-read the README — or whichever front-door doc the user names — as several different readers in turn, not as the author. The author knows what every line means; a first-time reader does not. The job is to surface where a specific audience gets confused, under-served, or bounces, and to propose concrete fixes.
This is an INTERNAL dev skill (not shipped to consumers). It complements the
readme-brevity and docs-quality rules: those govern length/polish; this checks
whether the content actually lands for who it's for.
How to run it
- Read the target doc in full (default:
README.md; honor a path the user gives). Also skim the docs it links to, so "the README promises depth the doc doesn't deliver" is checkable. - For each persona below, do a cold read — adopt that reader's goals, vocabulary, and patience. Ask their questions, not yours.
- Produce the report in the format at the end. Be specific: quote the exact line, name the exact fix. Vague notes ("could be clearer") are useless.
The personas
Run all of these unless the user scopes to a subset.
1. Claude Code user (the primary audience)
Already lives in Claude Code; skimming on a laptop between tasks. Wants the WOW in the first screen and a copy-paste install in seconds.
- Does the first screen land what vigiles does and why they'd care?
- Is the install path (
npx vigiles init, the agent prompt) above the fold and obviously runnable? - Does it speak their language (hooks, skills, CLAUDE.md, subagents) without over-explaining?
2. Codex user (the second-harness audience)
Uses OpenAI Codex / AGENTS.md, not Claude Code. Skeptical that this is "a Claude
thing."
- Is Codex support visible early, or buried/footnoted so they assume it's CC-only?
- Are the examples CC-only (CLAUDE.md,
claudeCLI) in a way that makes a Codex user feel like a second-class citizen? - Would they know
vigiles/codexexists and what works vs. what's a documented follow-on?
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.
- 9d ago First seen · 116 lines · 34 tokens per session scan A 3a1c0a8f587c
audience-check is a skill published in the GitHub repository zernie/vigiles (15 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 1,172 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
link-check
Verify @file references in AIWG skills and agents against the linking contract — per-file or corpus-wide, with optional auto-fix.
activity-log
Query and manage the unified .aiwg/activity.log chronological record of AIWG-managed workflow operations.
validate-metadata
Validate AIWG extension definitions against the metadata schema and report errors with field names, line numbers, and remediation hints.
eval-agent
Run evaluation tests against an agent to assess quality and archetype resistance.
hook-enable
Enable the AIWG context hook in platform context files without re-deploying.
mention-lint
Lint @-mentions for style consistency and correctness.