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acronym-unpacker

Intelligent medical abbreviation disambiguation tool that resolves ambiguous acronyms using clinical context, specialty-specific knowledge, and document-level semantic analysis.

48

Quality

60%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Evidence Insight/acronym-unpacker/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%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 genuinely useful material — usage commands, parameter table, supported-acronym table, output example, and the linked audit reference — is solid, but it is buried in heavy generic boilerplate, duplicated/broken cross-references ("See `## Features` above" appears before that section), a bogus example path, and contradictory dependency instructions. Cutting the template filler and consolidating the operational steps would roughly halve the file while improving clarity.

Suggestions

Delete or move the generic template sections (Risk Assessment, Security Checklist, Lifecycle Status, Output Requirements, Input Validation, Response Template, the first References section) — they contain no acronym-specific knowledge and consume most of the token budget.

Remove the broken "See `## Features`/`## Usage`/`## Workflow` above" cross-references (they point at sections that appear later or duplicate content) and the fabricated run path "cd \"20260318/scientific-skills/...\"", replacing it with a path relative to the skill bundle.

Reconcile the Dependencies contradiction ("pip install -r requirements.txt" vs "No external dependencies required") and replace the abstract 5-step Workflow with the concrete validated sequence already present in pieces: py_compile quick check → --help → run with context → review output.

DimensionReasoningScore

Conciseness

Roughly 200 of the ~270 body lines are generic template boilerplate (Risk Assessment, Security Checklist, Lifecycle Status, Output Requirements, Response Template, Input Validation, Error Handling, duplicated References sections) that adds no skill-specific knowledge, and several sections are pure filler ("See `## Features` above for related details."). This matches anchor 2 (noticeably verbose, several unnecessary padded sections) rather than 1, since it does not explain concepts Claude already knows like a format or library primer — it is process padding rather than educational padding.

2 / 5

Actionability

The Usage section provides copy-paste-ready commands ("python scripts/main.py PID --context cardiology", "--list"), a concrete parameter table, and a real output example, satisfying anchor 4 (mostly executable, minor gaps). It falls short of 5 because the run example opens with a bogus hardcoded directory ("cd \"20260318/scientific-skills/Evidence Insight/acronym-unpacker\"") and the Dependencies section says "pip install -r requirements.txt" while also claiming "No external dependencies required" with no requirements.txt in the bundle.

4 / 5

Workflow Clarity

A sequence exists, but the formal "Workflow" section is generic ceremony ("Confirm the user objective, required inputs..." / "stop early if the task would require unsupported assumptions") with abstract checkpoints, while the concrete operational flow (Quick Check py_compile → --help → run → review output) is scattered across "Quick Check", "Prerequisites", and "Usage" and never assembled into one path. This fits anchor 3 (sequence present but checkpoints missing or implicit); the skill is neither destructive nor batch, so the hard cap below 3 does not apply, but the checkpoints are not tied to concrete commands in the workflow itself.

3 / 5

Progressive Disclosure

The bundle structure is simple (scripts/main.py plus a one-level-deep, clearly linked references/audit-reference.md), but the body duplicates navigation: an early "References" section lists phantom non-file items ("Medical abbreviation standards", "Clinical terminology sources", "Context disambiguation methods") and a second "References" section links the real file, creating confusion about what actually exists. This matches anchor 3 (some structure, could be better organized, references not consistently signaled).

3 / 5

Total

12

/

20

Passed

Description

53%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 communicates a clear, specific purpose in the medical acronym domain, but it omits any "Use when..." trigger guidance and leans on a single capability statement with mild buzzword padding ("Intelligent", "document-level semantic analysis"). Adding explicit use-when triggers and enumerating the tool's concrete capabilities would lift it substantially.

Suggestions

Append an explicit trigger clause, e.g. "Use when the user mentions medical acronyms or abbreviations, asks to expand/disambiguate terms like PID or MI, or needs acronym expansion in clinical notes or research documents."

Enumerate the tool's actual capabilities from the body (expand single acronyms with confidence scores, rank expansions by clinical context, list the known acronym database) instead of one broad mission statement.

Trim buzzword padding ("Intelligent", "document-level semantic analysis") in favor of concrete, user-sayable terms like "expand medical acronyms".

DimensionReasoningScore

Specificity

The description names its domain ("medical abbreviation disambiguation tool") and one concrete action ("resolves ambiguous acronyms") supported by mechanism phrases ("clinical context, specialty-specific knowledge"), matching the anchor for a domain plus 1-2 concrete actions that are not comprehensive. It falls short of anchor 4 because it never enumerates several distinct actions the tool performs (expanding single acronyms, listing known acronyms, batch document expansion), and is above anchor 2 because the action described is specific rather than generic.

3 / 5

Completeness

The "what" is clearly stated ("resolves ambiguous acronyms using clinical context, specialty-specific knowledge, and document-level semantic analysis"), but there is no "Use when..." clause or equivalent trigger guidance, which caps completeness at 3 per the judging guidelines. It cannot score 4 without an explicit when-to-use statement, and is well above 2 because the what is concrete rather than vague.

3 / 5

Trigger Term Quality

Relevant keywords like "medical abbreviation", "acronyms", "disambiguation", and "clinical context" are present, but the natural phrases a user would actually say ("expand", "spell out", "define", "look up an acronym") are missing. This sits at anchor 3 (some relevant keywords, missing common variations or synonyms) — below anchor 4's "good keyword coverage" and above anchor 2's generic-only keywords.

3 / 5

Distinctiveness Conflict Risk

"Medical abbreviation disambiguation" carves out a fairly distinct niche unlikely to fire for unrelated skills, fitting anchor 4 (mostly distinct, minor overlap risk with closely related skills such as general terminology or glossary tools). It does not reach anchor 5 because the absence of explicit trigger phrases leaves somewhat weaker differentiation from generic abbreviation-handling skills.

4 / 5

Total

13

/

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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