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 Wang-Cankun/cankun-skills --skill known-unknownsgit clone --depth 1 https://github.com/Wang-Cankun/cankun-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/wang-cankun/cankun-skills/known-unknowns)<a href="https://agentmods.dev/skills/wang-cankun/cankun-skills/known-unknowns"><img src="https://agentmods.dev/badge/skills/wang-cankun/cankun-skills/known-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/wang-cankun/cankun-skills/known-unknowns"><img src="https://agentmods.dev/badge/skills/wang-cankun/cankun-skills/known-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 | $0.00091 | $0.01055 |
| Opus 5 | $0.00046 | $0.00528 |
| Sonnet 5 | $0.00018 | $0.00211 |
| Haiku 4.5 | $0.00009 | $0.00105 |
Grade A, and why
known-unknowns 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.
This is a copy
92% identical to known-unknowns — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Orientation through the Rumsfeld Matrix
Conduct the deliberation in the user's language.
Use the matrix to orient a multi-turn deliberation. The topic starts mostly dark; each turn enables one aspect-seeing by making one relevant part more nameable, visible, or testable.
| they know it | they don't know it | |
|---|---|---|
| aware of it | known knowns — stated and confirmed | known unknowns — named gaps |
| unaware of it | unknown knowns — operative but unrecognized | unknown unknowns — options they'd never ask about |
The matrix tracks awareness; tacitness tracks articulability. Classify each entry by awareness, and treat difficulty articulating an already-recognized judgment as a separate named gap. Split descriptions whose parts belong in different cells.
Use the marks as lifecycle shorthand: ✓ settled · ? named question · ! recognition candidate · ~ unexplored frontier. The map always renders as this 2×2 table — every entry sits in its cell with its mark inline; a flat mark list is not a map.
Choose the move by cell:
- Known knowns → confirm and compress. Restate what is settled, preserve only what matters downstream, and reopen it only when later evidence conflicts.
- Known unknowns → investigate. Name the gap and what would resolve it. When the gap is articulation, externalize: propose
!candidate representations that another person or model could act on, then test them against the user's already-recognized judgment. Otherwise use evidence, reasoning, or experiment. Run whatever probe you can run yourself before asking the user. - Unknown knowns → surface for recognition. Reflect patterns, assumptions, commitments, or practiced judgment already present in the user's words, behavior, or artifacts. Present grounded hypotheses as
!candidates so the user can recognize, reject, or refine them. Recognition beats recall. - Unknown unknowns → give a tour. Introduce an unmentioned region of the option space: what exists, when it fits, what it costs, and your own read.
Keep the move open-ended: the four cell moves are defaults, not a closed set. A turn may instead run a concrete probe such as a premortem or inversion, draw a distinction, offer a counterexample, or change scale — whatever best advances the chosen entry.
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.
- 11d ago First seen · 55 lines · 91 tokens per session scan A ed1ff7f87c33
known-unknowns is a skill published in the GitHub repository Wang-Cankun/cankun-skills (2 stars, last pushed 13d ago), licensed MIT. It adds 91 tokens to every session and 1,055 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to known-unknowns, differing in 7 lines, and is treated as a copy.
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