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 clamp-sh/analytics-skills --skill causal-evidence-checklistgit clone --depth 1 https://github.com/clamp-sh/analytics-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/clamp-sh/analytics-skills/causal-evidence-checklist)<a href="https://agentmods.dev/skills/clamp-sh/analytics-skills/causal-evidence-checklist"><img src="https://agentmods.dev/badge/skills/clamp-sh/analytics-skills/causal-evidence-checklist/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/clamp-sh/analytics-skills/causal-evidence-checklist"><img src="https://agentmods.dev/badge/skills/clamp-sh/analytics-skills/causal-evidence-checklist.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 | $0.00230 | $0.04257 |
| Opus 5 | $0.00115 | $0.02129 |
| Sonnet 5 | $0.00046 | $0.00851 |
| Haiku 4.5 | $0.00023 | $0.00426 |
Grade A, and why
causal-evidence-checklist 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.
How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Causal evidence checklist
Observational analytics is full of correlations that look causal and aren't. A deploy ships Tuesday, bounce rate jumps Wednesday, and the instinct is to roll back. Sometimes the deploy did it. Sometimes a marketing campaign landed the same day. Sometimes Wednesday is always like that. This skill encodes a 60-year-old epidemiology rubric — Bradford Hill's 9 viewpoints (1965) — as a checklist the agent fills before recommending an action.
Hill's original audience was epidemiologists deciding whether smoking caused lung cancer without the option of a randomized trial. The same constraint applies to most product analytics: you can't randomize a deploy across a population, so you reason from observational evidence and triangulate. The 9 viewpoints are how.
When NOT to use this
- The evidence is from a properly-randomized A/B test. Randomization handles most of these criteria automatically (temporality, specificity, confounding). Use
experiment-result-readerinstead. The checklist is for observational data where you can't randomize. - The user only wants an exploratory hypothesis, not a decision. This skill gates recommendations. If they're brainstorming what might explain a chart and are nowhere near acting, it's overkill — use
analytics-diagnostic-methodto build the hypothesis tree first. - The metric move is inside noise. If the "effect" is 1pp on n=200, there's nothing to explain yet. Send the user back to sample-size discipline (in
analytics-diagnostic-method) before causal reasoning. - The system has a known instrumented cause. If the deploy literally added a
try/catcharound the conversion event and conversions dropped, you don't need 9 criteria — you need to read the diff.
The methodology
Phase 1. State the claim precisely
Write the causal claim as one sentence: "X caused Y, where X = [specific change] and Y = [specific metric move]". If the user is vague ("the deploy broke things"), pin them down: which deploy, which metric, over what period, by how much. Without a specific X and Y, the checklist has nothing to score.
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 · 234 lines · 230 tokens per session scan A f29c1675683d
causal-evidence-checklist is a skill published in the GitHub repository clamp-sh/analytics-skills (8 stars, last pushed 3mo ago), licensed MIT. It adds 230 tokens to every session and 4,257 once invoked, about $0.0011 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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