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signal-capture

Detect and capture technical preferences, naming conventions, and architectural practices from user messages into Packmind standards. Trigger when user prescribes HOW to code through direct statements (e.g., "Use snake_case for columns", "Always use async/await") or suggestive patterns like questions ("Could we use X instead?"), code review feedback ("This would be clearer with X"), and comparative statements ("Use X instead of Y"). Does NOT trigger for feature requests, bug reports, or general implementation tasks without coding preferences.

68

Quality

81%

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SKILL.md
Quality
Evals
Security

Quality

Content

71%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.

Highly actionable and clearly sequenced, with concrete YAML templates and validation prompts. Its main weakness is verbosity from repeated trigger lists and long inlined examples that belong in separate reference files.

Suggestions

De-duplicate the trigger conditions: present them once (e.g. in 'When to Trigger') and have the checklist reference that section instead of restating each item.

Move the four detailed example scenarios (and/or the full YAML schema) into a references/ file, keeping only one compact inline example in SKILL.md.

Tighten the 'Important Guidelines' section, which largely restates steps already covered in the Workflow, to reduce token cost.

DimensionReasoningScore

Conciseness

Mostly useful content but noticeably padded: trigger conditions are repeated across the checklist, 'When to Trigger', and the description, and four full-YAML example scenarios add length that could be tightened.

3 / 5

Actionability

Provides a concrete YAML schema with per-operation field requirements, exact validation-prompt wording, and full good/bad code examples that are copy-paste ready across common cases.

5 / 5

Workflow Clarity

Clear 4-step sequence (Detection, Validation, Log, Confirmation) with an explicit user-approval checkpoint and refusal/clarification handling, though error-recovery feedback loops are minimal.

4 / 5

Progressive Disclosure

Good section headers structure the 320-line body, but example scenarios and the full YAML schema that could live in separate reference files are inlined, and no bundle references exist.

3 / 5

Total

15

/

20

Passed

Description

91%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 with excellent natural trigger phrases and an explicit what/when/non-trigger structure. Slightly limited action breadth and minor overlap risk keep it just short of a perfect score.

DimensionReasoningScore

Specificity

Lists several concrete actions ('Detect and capture technical preferences, naming conventions, and architectural practices ... into Packmind standards') with example triggers, though the core verbs are limited to detect/capture.

4 / 5

Completeness

Explicitly answers both 'what' (detect and capture preferences into Packmind standards) and 'when' ('Trigger when user prescribes HOW to code ...'), plus an explicit non-trigger boundary.

5 / 5

Trigger Term Quality

Comprehensive coverage of natural user phrases ('Use snake_case for columns', 'Could we use X instead?', 'This would be clearer with X', 'Use X instead of Y') including direct, suggestive, and comparative variants.

5 / 5

Distinctiveness Conflict Risk

Clear niche with distinct triggers and an explicit non-trigger list, but some overlap risk with general code-review or coding-assistance skills.

4 / 5

Total

18

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
cteyton/packmind
Reviewed

Table of Contents

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