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reproducible-analysis

Machine-readable journal format for reproducible data analysis operations

59

Quality

67%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./.claude/skills/skills/reproducible-analysis/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 a well-structured, highly actionable reference with concrete schemas, named tools, explicit validation checkpoints, and failure-handling guidance. Its main gaps are a placeholder in the replay script and a monolithic length that could benefit from splitting detail into reference files.

DimensionReasoningScore

Conciseness

The body is dominated by lean tables, JSONL, and a bash script with little concept-padding, though prose sections like 'Core Principle' and 'Integration with GenAI Disclaimer' could be trimmed slightly, fitting the 'efficient with minor over-explanation' anchor above the midpoint.

4 / 5

Actionability

Concrete JSONL schemas, named MCP tools (mcp__qsv__qsv_stats, mcp__qsv__qsv_sqlp), and a mostly-executable bash replay script give strong actionable guidance; the replay script leaves core tool-replay logic as a placeholder ('tool-specific replay logic here'), keeping it just below fully copy-paste ready.

4 / 5

Workflow Clarity

The Journal Lifecycle (Starting/During/Closing) and Verification Protocol are explicitly sequenced with validation checkpoints ('If mismatch, flag the step and stop') and failure-retry feedback ('log both attempts'), matching the anchor for clear sequences with explicit validation and feedback loops.

5 / 5

Progressive Disclosure

The single file is well-organized with clear section headers and no nested references, but at ~185 lines with no bundle files it could offload the replay script or integration table into reference files; this fits 'good structure with minor organization gaps' rather than the ideal one-level-deep reference split.

4 / 5

Total

17

/

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 cleanly states a specific niche (a machine-readable reproducibility journal) in third person but lacks any explicit trigger guidance, which caps its completeness and weakens trigger discoverability. It is distinct and not vague, yet falls short of the strongest examples because it never tells Claude when to reach for it.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when the user asks to make an analysis reproducible, audit data operations, or verify a data workflow end-to-end.'

Include concrete actions in the description (e.g. 'log each operation with hashes, replay steps, and verify bit-identical outputs') rather than only naming the artifact.

Add natural trigger synonyms and file cues users would actually say, such as 'reproducibility journal', '.journal.jsonl', or 'audit my analysis'.

DimensionReasoningScore

Specificity

Names the domain ('reproducible data analysis operations') and a concrete artifact ('Machine-readable journal format'), but lists no concrete actions like logging operations or hashing inputs, matching the anchor that names a domain with 1-2 concrete concepts but is not comprehensive.

3 / 5

Completeness

It clearly states what the skill provides ('Machine-readable journal format for reproducible data analysis operations') but includes no 'Use when...' clause or equivalent explicit trigger guidance, which per the guidelines caps completeness at 3.

3 / 5

Trigger Term Quality

'reproducible data analysis' is a reasonably natural phrase, but the description omits common variations users would say ('log analysis steps', 'verify reproducibility') and any file extensions, fitting the anchor for some relevant keywords with missing synonyms.

3 / 5

Distinctiveness Conflict Risk

The 'reproducible data analysis' journal niche is mostly distinct with only minor overlap risk against closely related data-analysis skills; it is not yet a fully carved-out niche with minimal conflict, so 4 rather than 5.

4 / 5

Total

13

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
dathere/qsv
Reviewed

Table of Contents

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