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 agentmods add skills/lancegui/causal-powers/question-framingnpx skills add lancegui/causal-powers --skill question-framinggit clone --depth 1 https://github.com/lancegui/causal-powersWhat 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 | $0.00214 | $0.03123 |
| Opus 5 | $0.00107 | $0.01562 |
| Sonnet 5 | $0.00043 | $0.00625 |
| Haiku 4.5 | $0.00021 | $0.00312 |
Grade A, and why
question-framing 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 2d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Question Framing
Overview
The most expensive analytics mistake is not a wrong number — it's a right answer to the wrong question. It survives every validation check, reconciles perfectly, reproduces exactly, and is still useless, because the metric measured something other than what the decision needed.
This is the analytics counterpart of brainstorming a feature before building it — nail down what you're actually being asked, and what a good answer would change, before you load data.
Core principle: Define the estimand and the decision before you touch the data — because once you see the data, your definition will quietly bend to fit what's easy to compute.
The framing brief
Produce a short, complete brief — not a sprawling document — answering the elements below; for general/exploratory work it also fixes the data, approach, and deliverable (The plan below). Each one is a place analyses go wrong:
- The decision. What action does this number inform, and who takes it? If no decision rides on it, scope it down or drop it. "Interesting" is not a spec.
- The estimand / metric, exactly. Not "engagement" but "median sessions per 7-day-active user, per calendar week, in the US." Not "the effect of the pricing change" but "the change in 30-day retention for users who saw the new price vs. those who didn't." Pin the numerator, denominator, unit, and time window.
- Population and filters. Who is in and who is out? New vs. existing? Which date range? Which segments? Every filter is an assumption — name it.
- Unit of observation. Per user? per session? per transaction? per user-week? Most double-counting and most wrong denominators trace to a fuzzy unit of analysis.
- What would change the answer / decision. What result would flip the decision? If any number leads to the same action, you don't need the analysis. This also tells you the precision you actually need.
For a causal question, add three more and hand off to causal-identification:
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.
- 2d ago First seen · 120 lines · 214 tokens per session scan A a25b7fb0d2e2
question-framing is a skill published in the GitHub repository lancegui/causal-powers (2 stars, last pushed 9d ago), licensed MIT. It adds 214 tokens to every session and 3,123 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.
Other skills, from other repositories
audit-reproducibility
Enforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs. Report PASS/FAIL per claim against tolerance thresholds. Use before submission and before releasing a replication package.
diagnose
Root-cause a failing or wrong empirical result with a disciplined reproduce → minimise → hypothesise → instrument → fix loop, instead of guessing-and-poking. Use when the user says "why is my regression wrong", "this number changed", "my script errors out", "the result won't reproduce", "debug this", "this estimate…
capture-environment
Snapshot the computational environment for a replication package — detects the analysis stack (R / Stata / Python) and emits the right lockfiles (renv.lock + sessionInfo.txt, requirements.txt / environment.yml / uv.lock, Stata version + ado package list), records seeds and RNG kind, optionally writes a pinning…
checkpoint
Save a structured state snapshot before stopping or handing off. Captures the active plan, recent decisions, file pointers (with line numbers), open questions, and the next 1–3 actions into a checkpoint file under qualityreports/checkpoints/. Optionally proposes [LEARN] entries to add to MEMORY.md. Use when user says…
coauthor-brief
Generate a co-author / collaborator handoff brief for a multi-author, multi-machine project — summarizing what changed since the last brief (git delta), the current state of each artifact (manuscript, analysis, slides), open questions, how to reproduce locally, and any restricted-data access steps. Use when user says…
data-management-plan
Draft a funder-compliant Data Management Plan (NSF DMP, NIH DMS Policy 2023, ERC, Horizon Europe) by composing the confidential-data and environment-capture primitives. Sections cover data description, formats/metadata, storage/backup, access/sharing, preservation/archiving, and roles. Use when user says "data…