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causal-detective

Challenge causal claims through structured threat assessment, counterfactual reasoning, and CausalPy falsification checks. Use when validating whether a causal effect is real or when the user asks "is this effect real?" or "can I trust this result?"

Quality

81%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

67%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a lean, well-structured instruction skill with a clear workflow, a useful threat-to-check mapping table, and a concrete output pattern. Its main defect is that all three linked reference files are missing from the bundle, breaking progressive disclosure, and a minimal runnable check example would strengthen actionability.

Suggestions

Create the three referenced files (reference/counterfactual_analysis.md, reference/threat_catalog.md, reference/falsification_tests.md) or remove the References section so no links are broken.

Add a minimal executable example of running one CausalPy check (e.g. importing cp.checks and applying PreTreatmentPlaceboCheck to a fitted model) so users can go from threat mapping to execution.

Trim or merge the Core Questions section, which largely restates workflow steps 1-3, to tighten token efficiency.

DimensionReasoningScore

Conciseness

The body is efficient with no padding or explanations of concepts Claude already knows; the Core Questions section partially restates workflow steps 1-3 (e.g. "What is the counterfactual..." and "Is there something else that could affect both treatment assignment and the outcome?") and could be trimmed. Anchor 4 fits: minor trimmable redundancy but no real verbosity.

4 / 5

Actionability

Concrete guidance throughout: named check identifiers (cp.checks.PreTreatmentPlaceboCheck, cp.checks.LeaveOneOut, etc.), a fully specified output pattern, and a defined workflow. Minor gap: no example of actually invoking a CausalPy check or a worked claim; per the scoring notes an instruction-only skill is not penalized for lacking code when guidance is actionable, which keeps this above anchor 3.

4 / 5

Workflow Clarity

The 6-step Investigation Workflow is clearly sequenced with each step defined (frame, evaluate, hunt, map, interpret, communicate); step 5 acts as an interpretive checkpoint but there are no explicit validate-and-retry loops. Not a destructive/batch operation, so the validation cap does not apply; not 5 because checkpoints are implicit rather than explicit.

4 / 5

Progressive Disclosure

The body signals three one-level-deep references, but reference/counterfactual_analysis.md, reference/threat_catalog.md, and reference/falsification_tests.md do not exist in the bundle (no reference/ or references/ directory is present), so navigation fails at the first hop and no detail content is actually available. Scored against the actual bundle structure per the judging guidelines, this falls to the low anchor rather than the 3-anchor (references present and clearly signaled).

2 / 5

Total

14

/

20

Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: it states concrete capabilities in third person, names the domain and tool, and includes an explicit Use-when clause with natural trigger phrases. Only minor improvements possible in synonym coverage.

DimensionReasoningScore

Specificity

"structured threat assessment, counterfactual reasoning, and CausalPy falsification checks" names several specific concrete actions and the tool involved, matching the anchor for several specific actions with minor gaps (the verdict/reporting output is not mentioned). Not 5 because coverage is not fully comprehensive; not 3 because it goes well beyond 1-2 actions.

4 / 5

Completeness

The first sentence explicitly states what the skill does and the "Use when..." clause gives concrete trigger phrases, exactly matching the top anchor for both what AND when.

5 / 5

Trigger Term Quality

Natural user phrasings are present ("is this effect real?", "can I trust this result?", "validating whether a causal effect is real"), giving good keyword coverage. Not 5 because common synonyms like "confounding", "spurious", or "correlation vs. causation" are missing.

4 / 5

Distinctiveness Conflict Risk

A clear causal-inference validation niche with distinct quoted triggers; minimal overlap risk with other skills.

5 / 5

Total

18

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 3 missing

Warning

Total

15

/

16

Passed

Repository

pymc-labs/CausalPy

Path

causalpy/skills/causal-detective/SKILL.md

Reviewed

Table of Contents

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