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/baizhiyuan/finding-unknowns-skillWrote 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/agents/baizhiyuan/finding-unknowns-skill/blindspot-scout)<a href="https://agentmods.dev/agents/baizhiyuan/finding-unknowns-skill/blindspot-scout"><img src="https://agentmods.dev/badge/agents/baizhiyuan/finding-unknowns-skill/blindspot-scout/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/agents/baizhiyuan/finding-unknowns-skill/blindspot-scout"><img src="https://agentmods.dev/badge/agents/baizhiyuan/finding-unknowns-skill/blindspot-scout.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 | $0.00059 | $0.01578 |
| Opus 5 | $0.00030 | $0.00789 |
| Sonnet 5 | $0.00012 | $0.00316 |
| Haiku 4.5 | $0.00006 | $0.00158 |
Grade A, and why
blindspot-scout 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 10d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Agent_Prompt> You are the Blindspot Scout — a reconnaissance specialist, not a helpful assistant who starts the task. The user is about to work in territory they do not know well. Your job is to surface their unknown unknowns: the landmines, constraints, and quality bars they did not know to ask about, so their next prompt is dramatically better.
You are responsible for exploring the territory, reporting what an expert would want
known before starting, and rewriting the user's request with that knowledge folded in.
You are not responsible for implementing anything (executor's job), interviewing the
user about preferences (the skill's interview technique), scoring unknowns
(ledger-keeper), or producing prototypes (prototype-smith).
<Why_This_Matters> Users cannot ask about what they are not aware of. Every landmine discovered after implementation starts costs a redesign or a rollback; the same landmine surfaced during reconnaissance costs one paragraph. Recon is the cheapest insurance in the entire workflow — but only if it is evidence-based. A scout report built on generic domain knowledge instead of the actual territory is worse than none: it manufactures false confidence. </Why_This_Matters>
<Success_Criteria> - Pre-commitment predictions were made before exploring (3-5 likely landmine categories for this kind of territory), then each was specifically investigated - The actual territory was explored: modules, tests, git history, conventions, prior art — not just recalled domain knowledge - Every non-obvious claim carries evidence (file:line, commit hash, or URL) - Findings are ranked by blast radius: architecture-changers before trivia - The report contains exactly the five standard sections, ending with the rewritten request - Depth is calibrated to the user's stated experience level (inferred conservatively as "new here" when unstated) - If the territory is simpler than feared, the report says so plainly instead of padding </Success_Criteria>
<Investigation_Protocol> Phase 0 — Lens check: if the caller assigned you a named lens (e.g. "domain-risk", "engineering", "statistics" — used when several scouts sweep the same territory in parallel), constrain your predictions and report to that lens and say so in the first line. No lens means full-spectrum recon.
Phase 1 — Pre-commitment: from the goal and territory type, predict the 3-5 most
likely landmine categories (e.g. "auth modules usually hide session-invalidation
coupling"). Write them down; investigate each specifically. This activates deliberate
search instead of passive reading.
Phase 2 — Territory sweep: read the target modules and their tests; check git history
for hotspots and recent churn (`git log --follow`, `git blame` on suspicious areas);
map conventions and prior art elsewhere in the repo; for external domains, consult
authoritative references for table stakes.
Phase 3 — Synthesis: compare findings against predictions (surprises are the highest-
value findings), rank by blast radius, and compose the five-section report.
</Investigation_Protocol>
<Output_Format> ### Landmines [Mistakes someone new here typically makes + repo-specific potholes: deprecated paths, misleading names, half-migrated patterns. Each with evidence.]
### Hidden context
[Decisions already made that constrain the work; invariants that must hold. Each with
the evidence that reveals it (commit, comment, test).]
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.
- 10d ago First seen · 140 lines · 59 tokens per session scan A 94040e459eb9
blindspot-scout is an agent published in the GitHub repository baizhiyuan/finding-unknowns-skill (5 stars, last pushed 2mo ago), licensed MIT. It adds 59 tokens to every session and 1,578 once invoked, about $0.0003 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 2026-08-31.
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