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 lancewillett/ai-plugins --skill flake-to-factgit clone --depth 1 https://github.com/lancewillett/ai-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/lancewillett/ai-plugins/flake-to-fact)<a href="https://agentmods.dev/skills/lancewillett/ai-plugins/flake-to-fact"><img src="https://agentmods.dev/badge/skills/lancewillett/ai-plugins/flake-to-fact.svg" alt="Measured on agentmods" 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 | $0.00077 | $0.00785 |
| Opus 5 | $0.00039 | $0.00392 |
| Sonnet 5 | $0.00015 | $0.00157 |
| Haiku 4.5 | $0.00008 | $0.00078 |
Grade A, and why
flake-to-fact 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 7d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flake to fact
Apply the core rules below. When file tools are available, read the taxonomy for detailed classifications and examples. Classify the failure, not the test or the people involved.
Workflow
- Capture observed facts first: the exact failing signature, failed step or assertion, relevant logs, execution context, and run history. Keep facts separate from inferences.
- Describe recurrence separately from cause. Use
intermittentonly when the same signature has both passed and failed in comparable runs. Omit it for a one-off failure, even when the mechanism is commonly intermittent or the source calls it flaky or nondeterministic. It is a modifier, never a root cause. - Choose the narrowest supported domain, mechanism, and scope from the taxonomy. State all three in the label or expanded report. For step 6's
Unclassifiedfallback, useUnclassifiedinstead of a domain and omit the mechanism rather than guessing. - State confidence from the evidence:
- High: direct error, trace, or reproduction identifies the mechanism.
- Medium: several consistent signals identify the likely mechanism.
- Low: the signature or context suggests a domain, but a distinguishing diagnostic is missing.
- Treat retries as measurement, not proof. A passing retry can establish recurrence; it cannot prove the failure was harmless or identify its cause.
- If evidence cannot support a mechanism, write:
Unclassified [intermittent ]failure: <exact signature>; needs <specific diagnostic>. Includeintermittentonly with comparable pass/fail evidence. Do not invent a cause.
Label and output
Default to one plain-English line:
[Intermittent ]<domain>—<specific mechanism failure>: <exact signature or affected scope>
Examples: Test environment—database startup failure: MySQL container did not become healthy and Test design—brittle selector failure: visible-text lookup no longer matches the control.
For an expanded report, use this order:
What ships with it
2 files 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.
- 7d ago First seen · 51 lines · 77 tokens per session scan A 0aef441ebff9
flake-to-fact is a skill published in the GitHub repository lancewillett/ai-plugins (2 stars, last pushed 22d ago), licensed MIT. It adds 77 tokens to every session and 785 once invoked, about $0.0004 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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