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ai-shaped-readiness-advisor

Assess whether your product work is AI-first or AI-shaped. Use when evaluating AI maturity and choosing the next team capability to build.

66

Quality

81%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A thorough, highly actionable interactive assessment with excellent workflow sequencing and concrete prompts. Its weaknesses are verbosity from cross-section repetition and a monolithic structure that underuses progressive disclosure.

Suggestions

De-duplicate the competency definitions: define each maturity level once (in Steps 1-5) and have the Key Concepts section reference them rather than restating them.

Extract the long Step 8 action plans and per-competency level rubrics into reference files under references/ (e.g. action-plans.md, maturity-rubrics.md) and link to them one level deep from SKILL.md.

Tighten the repeated 'AI-first version / AI-shaped version' reframes across Steps 1-5 into a single shared template to reduce token cost.

DimensionReasoningScore

Conciseness

The body is highly actionable but ~920 lines with notable redundancy: the Key Concepts section restates competency definitions that are repeated again in Steps 1-5, and each step reframes an 'AI-first version / AI-shaped version' contrast.

2 / 3

Actionability

Provides exact agent opening prompts, an exact 8-question context sequence, explicit maturity-level rubrics, dependency logic, and week-by-week action plans with success criteria - fully executable guidance.

3 / 3

Workflow Clarity

Steps 0-9 are clearly sequenced with a mandatory facilitation protocol (one question per turn, progress labels, decision points) and explicit interruption/pause-resume handling.

3 / 3

Progressive Disclosure

No bundle files exist and all content lives in a single monolithic SKILL.md; the detailed action plans (Step 8) and per-level rubrics are candidates to split into reference files but remain inline.

2 / 3

Total

10

/

12

Passed

Description

85%

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 concise, third-person description that clearly states both capability and trigger conditions with strong distinctiveness. Its main weakness is reliance on coined 'AI-shaped' terminology that users are unlikely to voice naturally as a trigger.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('Assess whether your product work is AI-first or AI-shaped', 'evaluating AI maturity', 'choosing the next team capability to build') rather than vague language.

3 / 3

Completeness

Explicitly answers both what ('Assess whether your product work is AI-first or AI-shaped') and when ('Use when evaluating AI maturity and choosing the next team capability to build').

3 / 3

Trigger Term Quality

'AI maturity' is a natural term, but 'AI-shaped'/'AI-first' are coined framework jargon a user would not naturally say before knowing the skill; coverage is relevant but misses common phrasings.

2 / 3

Distinctiveness Conflict Risk

Targets a clear niche (AI-shaped readiness for product/PM teams) with distinct triggers, unlikely to fire for unrelated skills.

3 / 3

Total

11

/

12

Passed

Validation

81%

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

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (936 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

relative_links

Relative link issues: 11 suspicious

Warning

Total

13

/

16

Passed

Repository
deanpeters/Product-Manager-Skills
Reviewed

Table of Contents

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