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/predictive-modelingnpx skills add lancegui/causal-powers --skill predictive-modelinggit 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 | $0.00207 | $0.04213 |
| Opus 5 | $0.00103 | $0.02107 |
| Sonnet 5 | $0.00041 | $0.00843 |
| Haiku 4.5 | $0.00021 | $0.00421 |
Grade A, and why
predictive-modeling 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Predictive Modeling
Overview
Causal identification asks what an intervention did; structural estimation asks what a world we haven't seen would do. Prediction asks something narrower: given what I can observe about a unit now, what is likely true of it — so I can act on it. Score the claim, rank the account, flag the pharmacy. The deliverable is a number on a unit that drives a decision; the model is a means, not the point.
The signature failure of this discipline is leakage. A feature that encodes the answer — a timestamp that exists only after the outcome, a row that appears in both train and test — makes the model look brilliant in validation and fail on the units you actually act on. The harness says 0.99 AUC; deployment says coin flip, and nothing in the loss curve tells you so.
There is a twin failure: reading the model as causal. The boosting machine ranks "prior audit flag" as the top feature, and the analyst writes "prior flags drive diversion." It does no such thing — the feature correlates with the label; the model uses it to predict, not because it causes anything. A predictive model is a correlation engine pointed at an action, and treating its internals as mechanism is how a triage tool becomes a false causal story.
Core principle: a prediction is trustworthy only if it was evaluated the way it will be deployed. Everything below serves that one sentence — the split, the leakage probe, the calibration, the baseline — and a model that scores well under any other evaluation has told you nothing about the units you will act on.
Why are you modeling? — choose the arm before you fit
This is the fork. One question decides which of three families you're in, and they lead to three different workflows:
| What you actually need | The deliverable | Workflow |
|---|---|---|
| An effect that occurred — "did the policy work?", "what did the price cut do?" | A causal estimate inside the data | causal-identification |
| A world you haven't observed / a welfare number — "what price would the merged firm set?" | A counterfactual outside the data | structural-estimation |
| To predict / score / rank / flag units to drive an action | A number on a unit | here |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 138 lines · 207 tokens per session scan A 66c3da8454f0
predictive-modeling is a skill published in the GitHub repository lancegui/causal-powers (2 stars, last pushed 8d ago), licensed MIT. It adds 207 tokens to every session and 4,213 once invoked, about $0.0010 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…