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topic-evidence-mapper

Rapidly maps the evidence landscape around a medical topic by organizing major research streams, target populations, endpoints, methods, evidence density, and thin areas. Use this skill BEFORE medical-research-gap-finder — it provides the structured landscape that makes formal gap analysis more rigorous. Do not use for formal gap identification, study design, or protocol planning directly.

59

Quality

74%

Does it follow best practices?

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tessl review fix ./awesome-med-research-skills/Evidence Insight/topic-evidence-mapper/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

60%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 delivers a genuinely actionable, well-sequenced mapping workflow with concrete templates, validation gates, and a complete, verified reference bundle. Its main weakness is redundancy: the dense/thin-vs-gap distinction and the skill's purpose are restated many times across summary sections, hard rules, and "should not" lists, which wastes context without adding guidance.

Suggestions

Collapse "Skill Summary", "Skill Goal", "Core Function", "What This Skill Should Not Do", and the overlapping Hard Rules into one statement of purpose and one non-goals list — the dense/thin-vs-gap rule needs to appear once, not six times.

Replace the inlined A-J section list with the names only and delegate section content requirements fully to references/output-section-guidance.md, which already lists them.

Add a short sample output excerpt (e.g., one stream entry with a density rating and the training-knowledge label) to make the expected format concrete, and state explicitly whether mapping uses live search or training knowledge.

DimensionReasoningScore

Conciseness

The dense-vs-thin distinction is repeated at least six times (intro list, Core Function, "should not" list, Step 5, Hard Rules 1/3/4, "What This Skill Should Not Do"), and "Skill Summary", "Skill Goal", and "Core Function" all restate the same three-sentence intent. This is noticeably verbose with several padded, redundant sections rather than "some" tightening opportunities. It stays above anchor 1 because nothing explains concepts Claude already knows — the padding is self-repetition, not background education.

2 / 5

Actionability

Concrete, executable guidance for an instruction-only skill: a literal redirect script with [restatement]/[reason] placeholders, an exact mandatory label ("[Based on training knowledge — verify with a current literature search...]"), an exact sentence for multi-topic inputs, and ten named output sections. Not anchor 5 because it lacks a sample evidence-map snippet showing what a stream/density entry should look like, and never states whether live literature search is used or training knowledge only. Not anchor 3 because the quoted templates and enumerated structures go well beyond high-level hints.

4 / 5

Workflow Clarity

Seven clearly sequenced decision-logic steps, each bound to a named reference module, with an input-validation gate (out-of-scope redirect and stop), a completeness checkpoint ("If a relevant output section is produced without using the corresponding reference module, the output should be treated as incomplete"), and a per-step template requiring objective/key question/expected output/caution. Not anchor 5 because the final quality check ("make the user feel that the topic has become legible") is a soft criterion with no verifiable feedback loop. Not anchor 3 because validation checkpoints are explicit and well-placed; no destructive/batch cap applies.

4 / 5

Progressive Disclosure

All nine referenced bundle files exist under references/, references are one level deep, and each is signaled at point of use in the Reference Module Integration section and again in the relevant workflow step. Not anchor 5 because the body inlines the full A-J output structure and workflow-standard details that duplicate what references/output-section-guidance.md and workflow-step-template.md already carry — content is not cleanly split between overview and detail. Not anchor 3 because the overview-to-reference navigation is clear, complete, and every reference resolves to a real file.

4 / 5

Total

14

/

20

Passed

Description

78%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 with a clear, enumerated statement of what the skill does, an explicit workflow-position trigger tied to a named sibling skill, and explicit exclusions that sharply reduce conflict risk. The main gap is the absence of natural user-facing trigger phrases that would let a model fire the skill on a bare request like "map the evidence on X" without the gap-finder context.

Suggestions

Add a user-utterance trigger clause such as "Use when the user asks to map or survey the evidence/landscape of a medical research topic, or wants an overview before a review or gap analysis".

Include natural synonyms users actually say ("literature landscape", "scoping", "field overview") alongside the current technical terms.

Consider trimming the rationale clause ("it provides the structured landscape that makes formal gap analysis more rigorous") — the ordering instruction alone conveys it — to free tokens for trigger phrases.

DimensionReasoningScore

Specificity

"Rapidly maps the evidence landscape around a medical topic by organizing major research streams, target populations, endpoints, methods, evidence density, and thin areas" names the domain and enumerates several concrete mapping dimensions. It sits below anchor 5 because everything hangs off a single verb pair ("maps / organizing") rather than multiple distinct concrete actions, and above anchor 3 because the six enumerated dimensions give comprehensive, concrete coverage rather than just 1-2 actions.

4 / 5

Completeness

The "what" is clear and enumerated, and the "when" is explicit ("Use this skill BEFORE medical-research-gap-finder") with negative triggers ("Do not use for formal gap identification, study design, or protocol planning"). Not anchor 5 because the "when" covers only the workflow-ordering context relative to the sibling skill and lacks concrete user-utterance trigger phrases (e.g., "when the user asks for an evidence map of a topic"); not anchor 3 because an explicit 'Use...' clause with both positive and negative trigger guidance is present.

4 / 5

Trigger Term Quality

Terms like "evidence landscape", "medical topic", "research streams", "evidence density", "thin areas", and "formal gap analysis" are natural for the target audience, and the explicit skill-name trigger "BEFORE medical-research-gap-finder" is a strong routing cue. It falls short of anchor 5 because common user phrasings like "literature mapping", "scoping review", "field overview", or "map the evidence on X" are absent.

4 / 5

Distinctiveness Conflict Risk

"Use this skill BEFORE medical-research-gap-finder" plus explicit exclusions ("Do not use for formal gap identification, study design, or protocol planning") carve out a clear niche with minimal conflict risk against the closely related gap-finder skill. The scope boundary is stated explicitly enough that mis-triggering is unlikely.

5 / 5

Total

17

/

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

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

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