CtrlK
BlogDocsLog inGet started
Tessl Logo

disease-mechanism-evidence-map

Systematically maps mechanism evidence for a disease from molecules to pathways, cell types, tissues, biological consequences, and clinical phenotypes. Always use this skill when a user needs a layered mechanism evidence chain rather than a flat summary or immediate gap analysis. Formal literature citations must be real and verifiable.

69

Quality

85%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

77%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 skill is a well-architected, instruction-only mapping skill with a clear multi-step workflow, explicit validation gates, and clean progressive disclosure to real reference files. Its main weakness is verbosity from cross-section restatement of the same distinctions, which inflates the token budget without adding guidance.

Suggestions

Consolidate the repeated layer/distinction lists: state the molecular→pathway→cell→tissue→consequence→phenotype layers and the direct/indirect/inference labels once in Core Function or Output Requirements, then reference rather than re-enumerate them in Hard Rules and What This Skill Should Not Do.

Add one short worked example (a few lines of a sample layered evidence chain for a simple disease) so the output format is unambiguous and copy-adaptable.

Trim the Sample Triggers and Expected User Inputs overlap, since both sections cover similar triggering phrasings.

DimensionReasoningScore

Conciseness

The body is well-structured and avoids explaining concepts Claude already knows, but the same layer distinctions (molecular/pathway/cell/tissue/phenotype; direct/indirect/inference) are restated across Task, Skill Summary, Core Function, Output Requirements, Hard Rules, and What This Skill Should Not Do, creating noticeable redundancy.

3 / 5

Actionability

Concrete, specific guidance throughout: a 9-step decision logic, each step naming the exact reference module and action to perform, plus a mandatory output structure (sections A–K) and a literal input-validation redirect template; the only gap is the absence of a worked example of a finished evidence-chain output.

4 / 5

Workflow Clarity

A clear 9-step sequence is sequenced with an explicit validation checkpoint at the citation step (verify papers are real; if verification is incomplete, state so and do not present as formal evidence), plus a stop-and-redirect input-validation gate.

5 / 5

Progressive Disclosure

SKILL.md acts as an overview that signals 11 one-level-deep reference modules, each invoked at its relevant step; all referenced paths resolve to real files, content is appropriately split (rules in references, orchestration in SKILL.md), and navigation is clear.

5 / 5

Total

17

/

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.

The description is strong: third-person voice, a complete what-and-when statement, comprehensive coverage of the mapping layers, and explicit differentiation from neighboring skills. Its only mild weakness is trigger-term naturalness, which is slightly formal.

Suggestions

Add one or two more natural, user-spoken trigger phrases (e.g. "disease mechanism map", "how a disease works across evidence layers") alongside the clinical phrasing.

Consider naming the most common trigger surface (e.g. "when the user asks to map how a disease mechanism is supported") to broaden natural-keyword coverage.

DimensionReasoningScore

Specificity

Enumerates a comprehensive set of concrete mapping actions across the full evidence chain ("from molecules to pathways, cell types, tissues, biological consequences, and clinical phenotypes"), covering all six layers rather than just naming the domain.

5 / 5

Completeness

Explicitly answers both "what" (maps mechanism evidence across a layered chain) and "when" ("Always use this skill when a user needs a layered mechanism evidence chain rather than a flat summary or immediate gap analysis") with a concrete trigger condition.

5 / 5

Trigger Term Quality

Includes natural trigger phrasing ("mechanism evidence chain", "flat summary", "gap analysis") with good coverage, but the language leans clinical and omits some common synonyms a user might naturally say.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (disease mechanism evidence mapping) and explicitly contrasts itself against adjacent skills ("rather than a flat summary or immediate gap analysis"), giving it distinct triggers with minimal overlap risk.

5 / 5

Total

19

/

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

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.