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 baizhiyuan/finding-unknowns-skill --skill finding-unknownsgit 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/skills/baizhiyuan/finding-unknowns-skill/finding-unknowns)<a href="https://agentmods.dev/skills/baizhiyuan/finding-unknowns-skill/finding-unknowns"><img src="https://agentmods.dev/badge/skills/baizhiyuan/finding-unknowns-skill/finding-unknowns/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/baizhiyuan/finding-unknowns-skill/finding-unknowns"><img src="https://agentmods.dev/badge/skills/baizhiyuan/finding-unknowns-skill/finding-unknowns.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 | $0.00133 | $0.08988 |
| Opus 5 | $0.00067 | $0.04494 |
| Sonnet 5 | $0.00027 | $0.01798 |
| Haiku 4.5 | $0.00013 | $0.00899 |
Grade B, and why
finding-unknowns scanned grade B with 1 finding 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
`findingUnknowns.ambiguityThreshold` from `./.claude/settings.json` (project) then How it starts
The opening of the file, as written. The whole thing — 676 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Finding Unknowns
Distilled from Thariq Shihipar's essay "A Field Guide to Fable: Finding Your Unknowns" (@trq212 — https://x.com/trq212/status/2073100352921215386). Community skill; not affiliated with or endorsed by Anthropic.
<Use_When>
- The user is starting work in a new domain or an unfamiliar part of the codebase
- The task involves taste — "I'll know it when I see it" criteria that resist specification
- The task is long-horizon and a wrong early assumption would compound
- A previous attempt came back wrong and the spec, not the execution, is the suspect
- The user says "blindspot pass", "unknown unknowns", "interview me", "brainstorm directions", "quiz me", or "Cartographer mode" </Use_When>
<Do_Not_Use_When>
- The request is specific and trivial (typo, one-liner, obvious bug fix) — execute directly
- The user has a detailed spec with file paths and acceptance criteria — go build it
- The user wants ontology/entity-convergence tracking or OMC-native state/pipeline
machinery (autopilot/ralph/team handoff) — use
oh-my-claudecode:deep-interviewinstead; Cartographer covers ambiguity gating and an approval-gated bridge without that runtime - The user explicitly says "just do it" — respect that; offer one sentence of risk if material, then proceed </Do_Not_Use_When>
<Why_This_Exists> Model capability is no longer the bottleneck on long-horizon work; clarity is. Generic requirements gathering asks "what do you want?" — but users cannot answer for the unknowns they are not aware of. This skill's premise is that different kinds of unknowing need different instruments: blind spots need reconnaissance, tacit taste needs reaction to prototypes, nameable gaps need targeted questions, and comprehension needs examination. A single uniform interview under-serves all four. </Why_This_Exists>
<Execution_Policy>
- ALWAYS locate the unknown on the quadrant map (Phase 0) before choosing a technique
- Establish the user's starting point — where they are in their thinking, their experience with this problem and codebase. The same task needs different probing for an expert than for a first-timer
- Discovery ends at understanding. No discovery technique slides into implementation
- Never ask the user a question the territory can answer — explore the codebase first
- Interview via AskUserQuestion (clickable options). Up to 3 independent questions per round, ordered by blast-radius/regret; never bundle dependent questions — where one answer would change the next question's framing, ask those one at a time
- Architecture-changing unknowns outrank trivia, always
- "You have no significant unknowns here" is a valid and valuable result — say it plainly rather than manufacturing concerns
- Deviations get logged, not buried. Comprehension is the merge gate, not a green diff
- Prefer single-file self-contained HTML artifacts for anything the user must react to or review (prototypes, plans, quiz reports); markdown fallback for pure-terminal flows
- Delegate to companion agents when installed and the case is non-trivial; run inline otherwise. Every technique works standalone </Execution_Policy>
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 676 lines · 133 tokens per session scan B 85fb3e633fc4
finding-unknowns is a skill published in the GitHub repository baizhiyuan/finding-unknowns-skill (5 stars, last pushed 2mo ago), licensed MIT. It adds 133 tokens to every session and 8,988 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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