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data-research

Structured data research: search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate. Parameterized via YAML recipes for investor updates, donations, company updates, or any email-to-structured-data pipeline.

63

Quality

76%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./skills/data-research/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

81%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 an efficient, well-sequenced 7-phase pipeline with strong validation checkpoints and an explicit anti-hallucination integrity rule. It is mostly actionable and well-structured, with minor room to tighten phrasing and make the single external reference more clearly signaled.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence — terse bullet lists with no padding about what email or PDFs are — with only minor instances (e.g., "Brain first (maybe we already have this data)") that could be trimmed.

4 / 5

Actionability

Concrete guidance via named tools (put_raw_data, file_upload, add_link) and explicit dedup decision branches with a markdown output example, though exact tool-call syntax is not given, leaving minor gaps.

4 / 5

Workflow Clarity

A clear 7-phase sequence with explicit validation checkpoints (the EXTRACTION INTEGRITY RULE: save raw first, re-read from saved files) and a documented failure mode, providing strong feedback loops for a batch operation.

5 / 5

Progressive Disclosure

Well-organized sections with one-level-deep, clearly signaled references to built-in recipes at ~/.gbrain/recipes/ and skills/conventions/quality.md; no bundle files present, and the convention reference is a bare path with only light signaling.

4 / 5

Total

17

/

20

Passed

Description

71%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 lists multiple concrete actions, but it lacks an explicit "Use when..." trigger clause, capping completeness. It is mostly distinct with good natural keyword coverage.

Suggestions

Add an explicit "Use when..." clause naming concrete user phrases (e.g., "Use when the user wants to track investor updates, donations, or company metrics from email and web sources").

Expand trigger terms with a few more natural synonyms (e.g., "extract from email", "data dig", "tracker") to lift trigger-term coverage to comprehensive.

Narrow the broad "data research" label or pair it more tightly with the email-to-structured-data framing to reduce overlap with general research skills.

DimensionReasoningScore

Specificity

Lists five concrete actions — "search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate" — giving comprehensive coverage of what the skill does.

5 / 5

Completeness

The "what" is clear and concrete, but "when" is only weakly implied via the recipe examples with no explicit "Use when..." clause, which per the rubric caps completeness at 3.

3 / 5

Trigger Term Quality

Includes natural user phrases ("research", "track", "investor updates", "donations", "build a tracker") with good synonym coverage, though a few natural variations are absent from the description text itself.

4 / 5

Distinctiveness Conflict Risk

A clear niche (email-to-structured-data pipelines, canonical trackers) with distinct triggers; mostly distinct with minor overlap risk since "data research" is a somewhat broad label.

4 / 5

Total

16

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

relative_links

Relative link issues: 1 missing

Warning

Total

14

/

16

Passed

Repository
garrytan/gbrain
Reviewed

Table of Contents

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