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 coleam00/skills --skill opportunity-scangit clone --depth 1 https://github.com/coleam00/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/skills/coleam00/skills/opportunity-scan)<a href="https://agentmods.dev/skills/coleam00/skills/opportunity-scan"><img src="https://agentmods.dev/badge/skills/coleam00/skills/opportunity-scan/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/coleam00/skills/opportunity-scan"><img src="https://agentmods.dev/badge/skills/coleam00/skills/opportunity-scan.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 | $0.00095 | $0.02246 |
| Opus 5 | $0.00048 | $0.01123 |
| Sonnet 5 | $0.00019 | $0.00449 |
| Haiku 4.5 | $0.00010 | $0.00225 |
Grade A, and why
opportunity-scan 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Opportunity Scan — find what to change, from what actually happened
Reads your agent's own capabilities plus one target you choose, and recommends which primitive each finding should become. Agent-agnostic (Claude Code, Codex, PI, …). It maps what it finds to the full primitive palette (rules · skill · hook · subagent · MCP · automation/workflow), and it works for any agent because it learns that agent's capabilities first.
Two targets, one skill — this is the whole design:
- A run → the REACTIVE loop. Something just went sideways in a loop you ran. Point the scan at that run's artifacts and ask "what in the AI layer would have prevented this?" You fix the system, not the code.
- A window of logs → the PROACTIVE scan. Nothing is broken. Point it at weeks of sessions and ask "what do I keep doing by hand that should be encoded?"
Same skill, same output shape — you're just changing what it reads.
This is a discovery tool — what to change — NOT a quality eval (whether a built thing is good). Keep the two separate.
Inputs — required first, then optional
Read $ARGUMENTS as prose, not as positional slots. Only input 1 is required. Input 2 is free-form and will
contain spaces, so never split arguments on whitespace and never bind them by position — a steer typed without
quotes is still one steer. If something is missing, ask for it once, in a single message, not one question
at a time.
- What to scan (required) — exactly one of:
- A RUN (reactive) — the artifacts one loop left behind: the plan, the implementation report, an RCA, the PR body, the review output, the commits/diff. Add that run's session log too if you can point at it. These are already scoped to the run, so there's no session-hunting to do.
- A WINDOW OF LOGS (proactive) — where your agent keeps session logs, plus how far back. Examples: Claude
Code →
~/.claude/projects/+~/.claude/history.jsonl; Codex →~/.codex/sessions/; PI → your extension's log dir. Default window: the last 2 weeks.
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 · 125 lines · 95 tokens per session scan A 77944a4ad962
opportunity-scan is a skill published in the GitHub repository coleam00/skills (495 stars, last pushed yesterday), licensed MIT. It adds 95 tokens to every session and 2,246 once invoked, about $0.0005 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.
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