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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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SecuritybySnyk

Low

Low-risk findings worth noting

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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.

A well-structured pipeline skill: a clearly sequenced 7-phase workflow with explicit validation and feedback loops for batch extraction, lean prose, and concrete tooling references. The only notable improvements are an example recipe YAML and moving recipe details into a reference file.

DimensionReasoningScore

Conciseness

The body is lean and imperative with no explanations of concepts Claude already knows; the only trimming opportunities are the "Contract" section and opening paragraph, which partially restate the frontmatter description.

4 / 5

Actionability

Guidance is concrete throughout — recipe file path ("~/.gbrain/recipes/{name}.yaml"), specific tool names (put_raw_data, file_upload), explicit dedup match rules, and a worked output-format example — but an example recipe YAML block would make the recipe scaffolding fully copy-paste ready.

4 / 5

Workflow Clarity

The 7-phase sequence is explicit with validation checkpoints where they matter for batch operations: the numbered EXTRACTION INTEGRITY RULE (save raw first, re-read from saved files, never trust LLM working memory), the dedup gate before tracker updates, and a fail-improve loop that logs LLM fallbacks for regex improvement.

5 / 5

Progressive Disclosure

Sections are well organized at overview altitude with a one-level reference to skills/conventions/quality.md, and no bundle files exist to misplace; the minor gap is that recipe schemas and the three built-in recipes are described inline and could be split into a reference file for a cleaner overview.

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.

A strong, specific description that names five concrete pipeline actions and a clear parameterized niche. Its main weakness is that explicit "when to use" guidance is absent from the description text itself, left to the separate triggers field, capping completeness.

Suggestions

Append an explicit "Use when..." sentence to the description, e.g. "Use when the user wants to track structured data from emails or wants recurring data collection for investor updates, donations, or company metrics" — this lifts completeness from the capped 3.

Add natural synonym variations to the trigger terms, such as "log", "keep a list of", or "catalog", alongside the existing "track" and "research".

Consider narrowing the broadest triggers ("research", "data dig") toward the pipeline's actual purpose (e.g. "track investor updates", "expense tracker") to reduce conflict risk with general research skills.

DimensionReasoningScore

Specificity

The description lists five concrete, distinct actions ("search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate") plus parameterization via YAML recipes, which matches the comprehensive multi-action anchor rather than the minor-gaps anchor below it.

5 / 5

Completeness

The "what" is clear and concrete, but the description itself contains no "Use when..." clause or equivalent; trigger guidance is delegated to a separate frontmatter triggers list, so per the rubric cap the "when" is only weakly implied and completeness is capped at 3.

3 / 5

Trigger Term Quality

Natural phrases like "investor updates", "donations", "company updates", and "email-to-structured-data" are terms users would actually say, but coverage stops short of the comprehensive anchor, which also includes synonyms and common variations (e.g., "log", "catalog", "spreadsheet", "keep a list of").

4 / 5

Distinctiveness Conflict Risk

The "email-to-structured-data pipeline" niche with YAML recipes and canonical tracker pages is mostly distinct, but generic triggers like "research" and "track" create minor overlap risk with general research or note-taking skills, fitting the mostly-distinct anchor rather than the minimal-conflict anchor.

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.

Validation — 14 / 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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