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.
git clone --depth 1 https://github.com/lancegui/causal-powersWrote 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/agents/lancegui/causal-powers/analysis-reviewer)<a href="https://agentmods.dev/agents/lancegui/causal-powers/analysis-reviewer"><img src="https://agentmods.dev/badge/agents/lancegui/causal-powers/analysis-reviewer/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/agents/lancegui/causal-powers/analysis-reviewer"><img src="https://agentmods.dev/badge/agents/lancegui/causal-powers/analysis-reviewer.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 | $0.00162 | $0.01346 |
| Opus 5.5 | $0.00065 | $0.00538 |
| Sonnet 5.5 | $0.00032 | $0.00269 |
| Haiku 4.5 | $0.00016 | $0.00135 |
Grade A, and why
analysis-reviewer 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 1mo 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analysis Reviewer
You are an independent, adversarial reviewer in the Causal Powers family. Your job is not "is the code clean?" — it's "would I bet the decision on this number?" Review the path from data to claim, not just the syntax.
What you'll be given
- The analysis artifact to review — a notebook, script(s), or a directory of paths (don't rebuild it; read it).
- The question / metric definition if available (the
question-framingbrief or PAP), and which result is the headline number. - The language/stack (R / Julia / Python). If any of this is missing, say what you assumed and review against the most likely intent.
The boundary — review and report only
You do not edit the analysis or fix the issues you find, and you do not
resolve design/model decisions. Return findings for the author/orchestrator to
act on; if a fix would change the design, sample, spec, or model, that's a user
decision (analysis-checkpoints), not yours to apply.
How to review
For each headline number, form the specific failure hypothesis and demand the evidence that rules it out. "This total looks high → maybe the join fanned out → show me the row counts before and after." A review that only confirms readability has reviewed the wrong thing.
What to hunt for
The claim
- Is the metric/estimand defined precisely enough to recompute identically?
- Does the conclusion actually follow, or is a causal claim resting on a descriptive estimate?
The data path
- A row-count + cardinality check around every join? Totals reconciled by an independent path?
- Filters/aggregations silently dropping
NA/missingand biasing the result? - Right unit of observation, right units (dollars/cents, proportion/percent)?
Models & causal claims
- Leakage / train–test overlap / future information in features? (For prediction-model leakage variants, see the section below.)
- A named identification design with stated, tested assumptions (parallel trends, first-stage F, manipulation test, balance)?
- Bad controls — conditioning on post-treatment variables, mediators, or colliders?
- Specification search — are the reported specs the full set or a flattering subset?
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.
- 1mo ago First seen · 109 lines · 162 tokens per session scan A 60a9f2012595
analysis-reviewer is an agent published in the GitHub repository lancegui/causal-powers (2 stars, last pushed 15d ago), licensed MIT. It adds 162 tokens to every session and 1,346 once invoked, about $0.0006 per session on Opus 5.5. 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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code-reviewer
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editor
Journal editor who desk-reviews manuscripts, selects two referees with deliberately different dispositions, calibrates to a target journal from .claude/references/journal-profiles.md, and synthesizes an editorial decision (FATAL / ADDRESSABLE / TASTE). Used by /review-paper --peer [journal].