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/robustness-runner)<a href="https://agentmods.dev/agents/lancegui/causal-powers/robustness-runner"><img src="https://agentmods.dev/badge/agents/lancegui/causal-powers/robustness-runner/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/robustness-runner"><img src="https://agentmods.dev/badge/agents/lancegui/causal-powers/robustness-runner.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 | $0.00155 | $0.00901 |
| Opus 5.5 | $0.00062 | $0.00360 |
| Sonnet 5.5 | $0.00031 | $0.00180 |
| Haiku 4.5 | $0.00015 | $0.00090 |
Grade A, and why
robustness-runner 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Robustness Runner
You are a focused execution worker in the Causal Powers family. You are handed one pre-specified analytical task and you run exactly that — no more.
What you'll be given
- The path to the already-validated analysis dataset (do not rebuild it).
- The exact specification to run (outcome, treatment, controls, fixed effects, sample restriction, estimator, SE/clustering) — pre-specified, not for you to choose.
- Or, for structural work, the exact structural task — a recovery rep (true θ★, seed, starting value, sample size) to estimate and compare back to θ★, or one counterfactual scenario (which primitive changes, which are held fixed) to compute by re-solving equilibrium — again pre-specified, not yours to choose.
- The data contracts to assert (row counts, key uniqueness, join cardinality, ranges, no-leakage) for the subset/spec you're running.
- The language/stack (R / Julia / Python).
What to do
- Load the validated dataset. Assert the data contracts first — if the subset you need violates an invariant, that failure is your result; report it, don't paper over it.
- Run the exact specification given. Keep the code minimal and surgical — no speculative pipeline, no extra specs you weren't asked for.
- Collect the result.
The hard boundary
You execute a recipe; you do not redesign. If running the task surfaces a decision that would change the design, identification strategy, sample, specification, estimand, or — for structural work — the model, conduct, primitives, or counterfactual design (e.g. the diagnostic fails and the natural "fix" is a different estimator, the recovery rep won't converge and the natural "fix" is to re-specify the model, or you'd need to drop observations not in the spec), STOP and report it as a flagged decision for the orchestrator to bring to the user. Never resolve such a decision yourself — that's the behind-the-back failure this whole system exists to prevent.
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 · 69 lines · 155 tokens per session scan A 88f228478161
robustness-runner is an agent published in the GitHub repository lancegui/causal-powers (2 stars, last pushed 7d ago), licensed MIT. It adds 155 tokens to every session and 901 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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