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product-prioritization

Product strategy and feature prioritization — score features by user demand evidence, effort (human vs AI-assisted), strategic alignment, and market signal. Anti-sycophantic forcing questions to cut through opinion.

60

Quality

70%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/product-prioritization/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

82%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 highly actionable, with concrete scoring mechanics and ready-to-fill output templates across three modes, plus strong sequencing and clean self-contained structure. Conciseness is good with minor trimming opportunities, and a few explicit validation checkpoints could be added for the downstream commit/dismiss actions.

DimensionReasoningScore

Conciseness

The body is efficient and largely assumes Claude's competence — the scoring table, formula, and filled-in output templates earn their tokens — with only minor over-explanation in the Core principles section that could be tightened, fitting the 'efficient; minor instances of over-explanation' anchor.

4 / 5

Actionability

Provides an executable scoring formula, a dimension table with concrete evidence sources, forcing questions, and three usage modes with copy-ready output templates populated with example values (e.g. '12 requests', '~15% retention', '4h AI-assisted'), fully actionable per the instruction-skill guidance.

5 / 5

Workflow Clarity

Each usage mode is clearly sequenced with numbered steps, and the forcing questions (notably #5 'What would change your mind?') act as validation checkpoints; the destructive/batch cap does not strictly apply since output is recommendations, but a couple of explicit verify-before-acting checkpoints for dismiss/commit actions are missing.

4 / 5

Progressive Disclosure

A self-contained methodology with well-organized sections (Core principles, Forcing questions, Scoring framework, Usage modes A/B/C, Integration, Anti-patterns) and one-level, clearly signaled references to other skills (decision-capture); no nested or buried references, with only minor organization gaps.

4 / 5

Total

17

/

20

Passed

Description

58%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 concretely captures the skill's methodology but omits an explicit 'Use when…' trigger clause, capping completeness and leaving natural trigger phrases underrepresented. It is distinctive and reasonably specific.

Suggestions

Add an explicit 'Use when…' clause with natural trigger phrases such as 'Use when the user asks what to build next, whether a feature is worth building, or to prioritize a backlog or roadmap.'

Incorporate conversational trigger terms users actually say ('what should we build next', 'is this worth building', 'prioritize my backlog') alongside the domain terms to raise trigger-term quality.

Trim the abstract tagline ('Anti-sycophantic forcing questions to cut through opinion') or convert it into a concrete capability statement to keep specificity high.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions — 'score features by user demand evidence, effort (human vs AI-assisted), strategic alignment, and market signal' plus 'Anti-sycophantic forcing questions' — with only minor coverage gaps, fitting the 'lists several specific actions' anchor rather than the fully comprehensive 5.

4 / 5

Completeness

It clearly states what the skill does (scoring + forcing questions) but lacks any 'Use when…' trigger clause, so per the rubric cap completeness cannot exceed 3; the 'when' is only weakly implied.

3 / 5

Trigger Term Quality

Relevant terms appear ('Product strategy', 'feature prioritization', 'user demand', 'market signal') but the natural phrasing users actually say ('what should we build next', 'is this worth building', 'prioritize my backlog') is absent, matching 'some relevant keywords but missing common variations'.

3 / 5

Distinctiveness Conflict Risk

The combination of demand-evidence scoring, dual human/AI effort estimates, and anti-sycophantic forcing questions is a fairly distinct niche with minor overlap risk against generic product/strategy skills, matching the 'mostly distinct' anchor.

4 / 5

Total

14

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
nearai/ironclaw
Reviewed

Table of Contents

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