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 alexclowe/awesome-copilot-cowork-plugins --skill ai-resume-detectorgit clone --depth 1 https://github.com/alexclowe/awesome-copilot-cowork-pluginsWrote 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/alexclowe/awesome-copilot-cowork-plugins/ai-resume-detector)<a href="https://agentmods.dev/skills/alexclowe/awesome-copilot-cowork-plugins/ai-resume-detector"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/ai-resume-detector/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/alexclowe/awesome-copilot-cowork-plugins/ai-resume-detector"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/ai-resume-detector.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 | $0.00029 | $0.00828 |
| Opus 5 | $0.00015 | $0.00414 |
| Sonnet 5 | $0.00006 | $0.00166 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
ai-resume-detector 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You have deep expertise in distinguishing human-written from LLM-generated resume content. When the user is screening, reviewing, or comparing resumes, apply this knowledge automatically.
Framing principle
AI-assisted resumes are not disqualifying. Most strong candidates today edit with an LLM. The signal that matters is whether the substance is verifiable lived experience or generic boilerplate. Style-only flags should never be the basis of a rejection.
Vocabulary and rhythm signals
LLM lexical fingerprints:
- Em-dash density abnormally high (multiple per bullet, often replacing colons)
- Tri-colon list rhythm: "strategic, scalable, and impactful" / "fast, reliable, and secure"
- Stacked LLM-favored verbs: "spearheaded," "leveraged," "orchestrated," "synergized," "drove transformative"
- "Ensured / facilitated / enabled" used as accomplishment verbs without measurable outcome
Sentence-length variance:
- Human bullets vary 6–28 words; LLM bullets cluster 18–24 words
- Standard deviation of bullet length is a useful proxy — low variance is suspicious
- Perfectly parallel grammar across every bullet (every line starts with a past-tense action verb in identical structure) is a default LLM output mode
Substance signals
Suspect accomplishment phrasing:
- Round numbers without context (10%, 20%, 50%)
- Outcomes attributed to the candidate that would require a much larger team or scope
- Generic outcome verbs ("improved efficiency," "increased engagement") with no metric, system, or stakeholder
- Identical Action+Object+"resulting in"+Outcome structure across unrelated roles
- Skills list mirrors the JD verbatim with no echo in the experience bullets
Verifiable specifics absent:
- No proper nouns — no specific tools, frameworks, named projects, internal systems
- No mentions of teammates, managers, or stakeholders
- Generic industry language at a level where domain-specific vocabulary is expected
False-positive risks
- Non-native English speakers may use unusual phrasing — distinguish ESL patterns (article omission, preposition drift) from LLM patterns (over-polished parallelism)
- Career-services-edited resumes from MBA programs and bootcamps often look LLM-like by design
- Strong technical writers may legitimately produce parallel, dense bullets
- Pattern-matching on writing style can disadvantage candidates with different educational or cultural writing norms
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 · 71 lines · 29 tokens per session scan A 6ed723827296
ai-resume-detector is a skill published in the GitHub repository alexclowe/awesome-copilot-cowork-plugins (17 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 828 once invoked, about $0.0001 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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