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 dandye/ai-runbooks --skill audit-contentgit clone --depth 1 https://github.com/dandye/ai-runbooksWrote 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/dandye/ai-runbooks/audit-content)<a href="https://agentmods.dev/skills/dandye/ai-runbooks/audit-content"><img src="https://agentmods.dev/badge/skills/dandye/ai-runbooks/audit-content/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/dandye/ai-runbooks/audit-content"><img src="https://agentmods.dev/badge/skills/dandye/ai-runbooks/audit-content.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00026 | $0.00570 |
| Opus 5.5 | $0.00010 | $0.00228 |
| Sonnet 5.5 | $0.00005 | $0.00114 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
audit-content 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 2mo 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Audit Skill
Perform a comprehensive quality and maintenance assessment of documentation or content. This skill evaluates content against quality standards, checks for freshness, identifies maintenance needs, and provides actionable recommendations.
Inputs
PATH- The directory or file path to audit (e.g., "/docs")SEVERITY- (Optional) Minimum severity level to report: "low", "medium", "high" (default: "medium")CATEGORY- (Optional) Categories to audit: "all", "quality", "relevance", "links", "metadata" (default: "all")FIX_MODE- (Optional) Boolean, whether to suggest or apply automated fixes where possible (default: false)
Workflow
Step 1: Inventory & Freshness Check
Scan the target PATH to list all content assets.
- Check "Last Modified" dates.
- Identify outdated content (e.g., > 6 months old).
- Verify author/owner metadata.
Step 2: Quality Assessment
Evaluate content against quality metrics:
- Clarity & Readability: Is the content easy to understand? (e.g., plain language).
- Completeness: Does it cover the topic sufficiently?
- Accuracy: Are there broken links, deprecated terms, or incorrect instructions?
- Structure: Does it follow standard templates and formatting?
Step 3: Issues & Recommendations
Generate a report of identified issues, categorized by severity:
- High: Broken paths, critical misinformation, missing required sections.
- Medium: Outdated styling, poor readability, minor inaccuracies.
- Low: Typos, inconsistent formatting.
If FIX_MODE is enabled, generate or apply suggestions for fixes.
Required Outputs
A CONTENT_AUDIT_REPORT in markdown format containing:
- Summary: Total files, overall quality score, critical issues count.
- Detailed Findings: Table of issues per file with severity.
- Action Items: Prioritized list of recommended changes.
- Freshness Report: List of stale or outdated documents.
Quick Reference
- Purpose: Support content maintenance planning and quality improvement.
- Key Metrics: Quality Score (0-100), Freshness (Age in days), Link Health (% valid).
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.
- 2mo ago First seen · 62 lines · 26 tokens per session scan A 709cf32d2cbf
audit-content is a skill published in the GitHub repository dandye/ai-runbooks (132 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 570 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.
audit-content compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| audit-contentdandye/ai-runbooks | Skill | 132 | 2mo ago | Apache-2.0 | A | 26 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | yesterday | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 315k | 5d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |