causal

Analyze cause-and-effect relationships in the Semantica knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores.

Quality

78%

Does it follow best practices?

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Adds up to 20 points to the overall score

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Securityby

Passed

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tessl review fix ./plugins/skills/causal/SKILL.md

Quality

Content

90%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 concise, highly actionable, and well-structured with executable code for each subcommand and no conceptual padding. It is a strong, lean reference skill with only minor room for clearer cross-subcommand sequencing.

Dimension	Reasoning	Score
Conciseness	Lean and efficient: headers, executable code, and a one-line output description per subcommand, with no padding explaining what causal analysis or a knowledge graph is; every token earns its place.	5 / 5
Actionability	Fully executable copy-paste-ready Python with real imports and parameterized method calls covering the common cases for each subcommand.	5 / 5
Workflow Clarity	Three clearly separated subcommands each with unambiguous single-action guidance; no overarching sequence is needed since these are parallel read-only analysis options, but there are no explicit checkpoints tying them together.	4 / 5
Progressive Disclosure	Well-organized section-per-subcommand structure with no need for external references; just over the 50-line simple-skill threshold, so it does not quite reach the clean 5-anchor overview-with-one-level-refs pattern.	4 / 5
	Total	18 / 20 Passed

Description

66%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.

The description is specific and well-targeted to a clear niche, but it omits any explicit 'Use when' trigger guidance, which caps its completeness score. Adding a when-to-use clause would raise the overall description quality.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when the user asks about causal chains, interventions, counterfactuals, or causal influence in the Semantica knowledge graph.'

Vary the verbs per sub-task (e.g. 'Build causal chains, run interventions, trace counterfactuals, compute influence scores') to strengthen specificity.

Include common synonyms or phrasings users might say (e.g. 'root-cause analysis', 'what-if', 'impact analysis') to broaden trigger term coverage.

Dimension	Reasoning	Score
Specificity	Lists several concrete actions ('causal chains, interventions, counterfactuals, and causal influence scores') grounded in a named domain, though the verbs are mostly a single 'Analyze' with noun-list sub-tasks rather than distinct action verbs per operation.	4 / 5
Completeness	Has a clear 'what' (analyze causal relationships and the four listed operations) but no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the rubric guidelines.	3 / 5
Trigger Term Quality	Includes natural domain keywords a user would say ('cause-and-effect', 'causal chains', 'interventions', 'counterfactuals'), with good coverage but a few synonyms/variations missing.	4 / 5
Distinctiveness Conflict Risk	Tied to a specific 'Semantica knowledge graph' niche with distinctive causal triggers, leaving only minor overlap risk with other graph-analysis skills.	4 / 5
	Total	15 / 20 Passed

Validation

100%

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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: semantica-agi/semantica
Path: plugins/skills/causal/SKILL.md
Commit: 2a303cf

Reviewed: 1 day ago

Table of Contents

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