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competitive-teardown

Analyzes competitor products and companies by synthesizing data from pricing pages, app store reviews, job postings, SEO signals, and social media into structured competitive intelligence. Produces feature comparison matrices scored across 12 dimensions, SWOT analyses, positioning maps, UX audits, pricing model breakdowns, action item roadmaps, and stakeholder presentation templates. Use when conducting competitor analysis, comparing products against competitors, researching the competitive landscape, building battle cards for sales, preparing for a product strategy or roadmap session, responding to a competitor's new feature or pricing change, or performing a quarterly competitive review.

69

Quality

87%

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SecuritybySnyk

Low

Low-risk findings worth noting

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.

A well-structured, actionable body with a clear sequenced workflow, explicit validation checkpoints, and concrete data-collection guidance. The main weaknesses are incomplete disclosure of the bundle — three of five reference/script files are never linked, and the inline scoring rubric conflicts with the reference rubric's scale — plus minor verbosity from inlining content that also lives in reference files.

Suggestions

Link and reconcile the scoring systems: reference references/scoring-rubric.md from the Scoring Rubric section and align the inline 12-dimension 1–5 scale with the reference file's 1–10 weighted scale (or state explicitly which one governs).

Surface the two orphaned bundle files: mention scripts/competitive_matrix_builder.py (with its CLI invocation) in the workflow and references/competitive-analysis-frameworks.md in the Templates or analysis sections so they are discoverable.

Add a feedback loop to the validation checkpoints (e.g., "If a competitor has fewer than 20 reviews, substitute G2/Capterra reviews or note the gap in the evidence column") so data shortfalls have a defined recovery path.

DimensionReasoningScore

Conciseness

The body is dense and skill-specific — a 12-dimension scoring table, an example completed scoring row, abbreviated templates, and a copy-paste App Store query — with deliberate summaries deferring full detail to references ("Full executable scripts for each source are in references/data-collection-guide.md. Summaries of what to capture are below"). It is not a 5 because the ~50-line inline Data Collection Guide section and the full inline 12-dimension rubric duplicate content that also lives in reference files, and could be trimmed further.

4 / 5

Actionability

Mostly concrete and executable: a copy-paste iTunes Search API query with exact response fields ("Parse entry[].content.label for review text"), specific validation thresholds ("at least 3 sources per competitor", "at least 20 reviews"), and a worked example scoring row with real evidence. It falls short of 5 because the SEO and Social Media sections offer only capture-lists and defer all executable queries to the reference file, and the scripts/competitive_matrix_builder.py tool (which has a concrete CLI: "python competitive_matrix_builder.py competitors.json --format text") is never surfaced in the body.

4 / 5

Workflow Clarity

The six-step workflow is clearly sequenced and includes explicit validation checkpoints on the two data-critical steps ("Validation checkpoint: Before proceeding, confirm you have pricing data, at least 20 reviews, and job posting counts for each competitor"; "Every dimension should have a score and at least one supporting evidence note"). It does not reach 5 because there are no feedback/error-recovery loops (e.g., what to do when a source yields fewer than 20 reviews) and steps 4–6 have no checkpoints.

4 / 5

Progressive Disclosure

The two linked references (references/data-collection-guide.md and references/analysis-templates.md) exist, are one level deep, and are clearly signaled. However, scored against the actual bundle: references/scoring-rubric.md, references/competitive-analysis-frameworks.md, and scripts/competitive_matrix_builder.py are never referenced from SKILL.md, making 3 of 5 bundle files undiscoverable; worse, the inline 12-dimension 1–5 rubric conflicts with scoring-rubric.md's 1–10 weighted scale, creating two inconsistent scoring systems. This matches anchor 3 ("some structure but could be better organized") rather than anchor 4's "references mostly clear".

3 / 5

Total

15

/

20

Passed

Description

96%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 description: it answers what and when explicitly with concrete, third-person capability statements and a rich set of natural trigger phrases. The only weakness is minor trigger overlap with sibling product-strategy/market-research skills.

Suggestions

Tighten the trigger "preparing for a product strategy or roadmap session" to anchor it in competitive context (e.g., "preparing competitive input for a product strategy or roadmap session") so it does not collide with a sibling product-strategist skill.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions with comprehensive coverage: "synthesizing data from pricing pages, app store reviews, job postings, SEO signals, and social media" plus seven named output artifacts ("feature comparison matrices scored across 12 dimensions, SWOT analyses, positioning maps, UX audits, pricing model breakdowns, action item roadmaps, and stakeholder presentation templates"). No vague filler or buzzwords; it uses third person ("Analyzes") as required.

5 / 5

Completeness

Both questions are answered explicitly and concretely: the "what" is the full analysis-and-artifact pipeline in the first two sentences, and the "when" is a dedicated "Use when..." clause with seven concrete trigger scenarios. Matches the anchor-5 example structure exactly.

5 / 5

Trigger Term Quality

Natural trigger phrasings are comprehensive and varied: "conducting competitor analysis", "comparing products against competitors", "researching the competitive landscape", "building battle cards for sales", "responding to a competitor's new feature or pricing change", "performing a quarterly competitive review". These are phrases a product manager or strategist would actually say, including the niche term "battle cards". Not below 5 because no common synonym family is missing; not applicable above 5.

5 / 5

Distinctiveness Conflict Risk

Mostly distinct: terms like "battle cards", "positioning maps", and "quarterly competitive review" anchor a clear competitive-intelligence niche. However, the trigger "preparing for a product strategy or roadmap session" overlaps with general product-strategy skills (the body itself lists a sibling "Product Strategist" skill), and "researching the competitive landscape" could collide with a generic market-research skill — minor overlap risk with closely related skills, matching anchor 4 rather than anchor 5's "minimal conflict risk".

4 / 5

Total

19

/

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

alirezarezvani/claude-skills

Path

product-team/skills/competitive-teardown/SKILL.md

Reviewed

Table of Contents

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