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.

Quality

63%

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tessl review fix ./skills/ai-shaped-readiness-advisor/SKILL.md

Quality

Content

70%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 content is exceptionally actionable and well-sequenced with explicit checkpoints and feedback loops, but it is markedly verbose and monolithic, inlining a large volume of assessment rubric and action-plan content that would benefit from being split into reference files.

Suggestions

Move the five detailed action plans (Step 8) into separate reference files (e.g. plans/context-design.md, plans/agent-orchestration.md) and link to them, keeping SKILL.md as an overview.

Trim editorial/marketing prose ("cute vs. survival", "Critical Insight" commentary) and collapse the repeated Reality/Problem/Outcome sub-bullets per level into a tighter format.

Consider extracting the per-competency Level 1-4 rubric statements into a reference file so SKILL.md stays a navigable overview.

Dimension	Reasoning	Score
Conciseness	The 918-line body is noticeably verbose: editorial/marketing prose ("AI-first is cute... AI-shaped is survival"), repeated Reality/Problem/Outcome framing across 20 level blocks, and five full multi-week action plans, much of which could be tightened or split out, matching anchor 2.	2 / 5
Actionability	It provides exact copy-paste-ready prompts, an exact 8-question context sequence, fully-specified 1-4 level statements per competency, explicit decision/dependency logic, and concrete week-by-week action plans with success criteria, fitting anchor 5 for an instruction skill.	5 / 5
Workflow Clarity	The process is clearly sequenced (Step 0 context through Step 9 tracker) with explicit checkpoints (4-line summary after Q8, maturity profile, numbered decision points), feedback loops (interruption handling, pause/resume, mode behavior), and a 16-rule facilitation protocol, matching anchor 5.	5 / 5
Progressive Disclosure	It has clear section structure and well-signaled one-level references to sibling skills (context-engineering-advisor, workshop-facilitation), but it is a monolithic single file with large inline blocks (five full action plans, detailed per-level rubrics) that would be better split into separate files, fitting anchor 3.	3 / 5
	Total	15 / 20 Passed

Description

57%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 clear, distinctive, and answers both what and when, but it is written in second person and uses niche framing language instead of natural user trigger phrases, which caps specificity and trigger quality.

Suggestions

Rewrite in third person to avoid the second-person voice penalty (e.g. "Assesses whether a team's product work is AI-first or AI-shaped").

Add concrete natural trigger phrases users would actually say (e.g. "Use when a user asks how AI-mature their team is, or which AI capability to invest in next").

Broaden trigger terms with synonyms ("AI readiness", "AI maturity assessment") to improve trigger coverage.

Dimension	Reasoning	Score
Specificity	The description names the domain and concrete actions ("Assess whether your product work is AI-first or AI-shaped", "evaluating AI maturity", "choosing the next team capability to build"), which fits anchor 3, but it uses second person ("your product work") so specificity is reduced by 1 per the voice guideline.	2 / 5
Completeness	It 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"), but the when trigger is somewhat abstract and could be more explicit, fitting anchor 4 rather than 5.	4 / 5
Trigger Term Quality	It surfaces relevant terms ("AI maturity", "AI-first", "AI-shaped", "team capability") but these are niche framing terms rather than the natural phrases users say, and common synonyms/variations are missing, matching anchor 3.	3 / 5
Distinctiveness Conflict Risk	The AI-first vs. AI-shaped framing is a distinctive niche with low conflict risk, but it sits among sibling AI-advisor skills (e.g. context-engineering-advisor), giving minor overlap risk consistent with anchor 4.	4 / 5
	Total	13 / 20 Passed

Validation

81%

Warnings & errors only

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

Validation — 13 / 16 Passed

Validation for skill structure

Criteria	Description	Result
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
Path: skills/ai-shaped-readiness-advisor/SKILL.md
Commit: 1b5a524

Reviewed: 4 days ago

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