PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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.
git clone --depth 1 https://github.com/mohitagw15856/pm-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/rules/mohitagw15856/pm-claude-skills/ai-content-audit)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/ai-content-audit"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/ai-content-audit/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/rules/mohitagw15856/pm-claude-skills/ai-content-audit"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/ai-content-audit.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 | $0.00132 | $0.01274 |
| Opus 5 | $0.00066 | $0.00637 |
| Sonnet 5 | $0.00026 | $0.00255 |
| Haiku 4.5 | $0.00013 | $0.00127 |
Grade A, and why
ai-content-audit 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 8d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Content Audit Skill
Teams that scaled content with AI are discovering the bill: libraries full of fluent, structurally identical, information-free pieces that readers bounce off, search engines quietly demote, and — worst — that erode the trust the good content earned. This skill audits the library for slop with named signals, triages it, and installs the gate that stops the refill.
What This Skill Produces
- An audited inventory with per-piece verdicts: keep / enrich / rewrite / delete-and-redirect
- The detection signals found, quoted — so verdicts are checkable, not vibes
- A triage plan sequenced by traffic and trust impact
- A publishing quality gate for AI-assisted content going forward
Required Inputs
Ask for (if not already provided):
- The corpus — pieces or URLs to audit (or a sample; state the sampling), with publish dates
- Performance data if available — traffic, engagement, rankings over time (the audit works without it, but verdicts get sharper)
- What the content is for — SEO, docs, thought leadership, support deflection (the quality bar differs)
- Production context — when AI-assisted publishing started, at what volume (the before/after seam is diagnostic gold)
Detection Method
Slop isn't "AI wrote it" — it's content with nothing inside. Audit each piece for the signals, quoting instances:
- Information density — the core test: delete every sentence that any competitor could have written, and measure what's left. Slop survives at <20%. Look for: zero proprietary data, zero named examples, zero opinions with an owner, zero specifics a reader could act on.
- Structural monoculture — the same skeleton repeating across pieces (intro-restating-the-title → 5 H2s → "in conclusion"); listicles whose items are definitions, not judgments; FAQ sections answering questions nobody asked.
- Hedged voicelessness — "it's important to note", "in today's fast-paced world", both-sides-ism on questions the brand should have a stance on; the absence of anything a lawyer would ever have flagged.
- Fluency without grounding — claims with no source, stats with no year, "studies show" with no study; internally contradictory sections (the tell of stitched generations).
- Reader evidence, where data exists — engagement collapse relative to the library's pre-AI baseline, rising pogo-sticking, ranking decay cohort-matched to the AI-volume era. Correlate verdicts with the seam from the production context.
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.
- 8d ago First seen · 77 lines · 132 tokens per session scan A 91f7915dc6c6
ai-content-audit is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,352 stars, last pushed 3d ago), licensed MIT. It adds 132 tokens to every session and 1,274 once invoked, about $0.0007 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 2026-09-03.
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