performing-causal-analysis

Estimate causal effects from existing data. Use when fitting or interpreting DiD, ITS, synthetic control, regression discontinuity, or other treatment-effect analyses, including robustness checks and counterfactual plots. For choosing a study design before analysis, use designing-experiments instead.

Quality

75%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./bundled/skills/performing-causal-analysis/SKILL.md

Quality

Content

57%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 well-structured and concise, but it stops short of executable guidance and lacks validation checkpoints, and its reference links point to files that are not present in the bundle. Adding a minimal runnable example and fixing/shipping the reference files would substantially raise quality.

Suggestions

Add a short copy-paste runnable example: the library import, the experiment class name, and a minimal instantiation+summary call so the workflow is executable rather than abstract.

Insert validation checkpoints in the workflow, e.g. verify the DataFrame has the expected treatment/time/post columns and confirm the model converged before interpreting summary() output.

Ship the referenced files under reference/ (diff_in_diff.md, interrupted_time_series.md, synthetic_control.md) or correct the link paths, so the References section actually resolves.

Dimension	Reasoning	Score
Conciseness	Lean body with no padding or beginner-concept explanations, but the opening paragraph and the 'It does not own...' line partially restate the description's scope and boundary guidance and could be trimmed.	4 / 5
Actionability	Names concrete methods (experiment.summary(), plot(), print_coefficients()) and a workflow, but gives no executable code, no library or class name, and 'Use the appropriate class (see References)' is only a high-level hint — and the referenced detail files are absent.	3 / 5
Workflow Clarity	A clear four-step sequence is present (Load Data, Initialize, Fit, Analyze) but there are no validation or verification checkpoints (e.g., confirm data format, check model convergence/assumptions), which caps clarity at 3.	3 / 5
Progressive Disclosure	Good section structure with a dedicated References list pointing to one-level-deep method files, but the referenced paths (reference/*.md) do not exist and no bundle directory was provided, so the disclosed navigation is non-functional.	3 / 5
	Total	13 / 20 Passed

Description

92%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, well-scoped description that clearly states capabilities, trigger conditions, and skill boundaries in third person. The only minor gap is use of abbreviations (DiD, ITS) where full-form synonyms would aid natural-language triggering.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions (estimate causal effects, fit/interpret analyses, run robustness checks, produce counterfactual plots) and comprehensively enumerates the supported methods (DiD, ITS, synthetic control, regression discontinuity).	5 / 5
Completeness	Explicitly states what it does (estimate causal effects from existing data, with the listed methods and outputs) and when to use it ('Use when fitting or interpreting...'), with concrete trigger phrases.	5 / 5
Trigger Term Quality	Strong keyword coverage of terms a causal-inference user would say (DiD, ITS, synthetic control, regression discontinuity, robustness checks, counterfactual plots), but relies on abbreviations rather than full-form synonyms like 'difference-in-differences' or 'interrupted time series'.	4 / 5
Distinctiveness Conflict Risk	Clear niche (treatment-effect estimation on existing data) with explicit boundary guidance routing design-time questions to 'designing-experiments', minimizing conflict risk.	5 / 5
	Total	19 / 20 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
relative_links	Relative link issues: 3 missing	Warning

	Total	15 / 16 Passed

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/performing-causal-analysis/SKILL.md
Commit: d5ae560

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.