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 human-avatar/skills-for-humanity --skill s4h-probability-base-rate-anchoringgit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-probability-base-rate-anchoring)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-probability-base-rate-anchoring"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-probability-base-rate-anchoring/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/human-avatar/skills-for-humanity/s4h-probability-base-rate-anchoring"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-probability-base-rate-anchoring.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 | $0.00064 | $0.01038 |
| Opus 5 | $0.00032 | $0.00519 |
| Sonnet 5 | $0.00013 | $0.00208 |
| Haiku 4.5 | $0.00006 | $0.00104 |
Grade A, and why
s4h-probability-base-rate-anchoring 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 9d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Probability Base Rate Anchoring
The most common forecasting error is treating every situation as unique and ignoring what usually happens. Kahneman called this neglecting the outside view. Before adjusting for what makes this situation special, you must first establish what happens in situations like this one. The base rate is the outside view — the answer to "what fraction of attempts like this one succeed?" — and it is almost always more informative than inside-view reasoning about this particular case.
Your Process
Step 1: State the Prediction or Estimate Name the specific outcome being predicted and the current estimate or intuition. What is being forecast and at what implied probability?
Framing check: Confirm the specific prediction before continuing. State what you've identified — the outcome being forecast, the current intuitive estimate, and the time horizon — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of the prediction and implied probability]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
Step 2: Find the Reference Class What category of similar situations does this belong to? This is the most important and contested step. The reference class should be: (a) large enough to have stable base rates, (b) genuinely similar in the ways that matter, (c) not cherry-picked to flatter the prediction. If multiple reference classes apply, note them all.
Step 3: Find the Base Rate How often does this outcome actually occur in that reference class? Use historical data where available. If the exact rate is unknown, estimate a range from the most similar data available. This is the outside view — state it plainly, even if it is uncomfortable.
Step 4: Adjust for Differentiating Factors What specific, verifiable features of this situation distinguish it from the reference class? For each: does it push the probability up or down from the base rate? Explicitly consider factors that cut against your prior, not only those that support it.
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.
- 9d ago First seen · 90 lines · 64 tokens per session scan A caa9077889a5
s4h-probability-base-rate-anchoring is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 1,038 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 2026-09-03.
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