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product-diagnosis

Diagnoses product health by cross-referencing Amplitude analytics (dashboards, charts, funnels, feedback, AI agent analytics), optionally Datadog (errors, latency, stack traces), and optionally Slack (qualitative feedback, bug reports, feature requests). Identifies what's broken, what's working, and what to do about it — with root causes, not just symptoms. Use when asked to "diagnose my product", "what's going on", "product health check", "what's broken", "where are users struggling", "give me a product diagnosis", or "what should I focus on".

76

Quality

95%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

88%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 a highly actionable, well-sequenced diagnostic workflow with concrete tool calls, validation checkpoints, and a quality-gated synthesis phase. Its only weaknesses are mild verbosity in the conceptual sections and a monolithic structure that could offload reference material into bundle files.

Suggestions

Move the RICE impact/confidence/effort anchor tables and the opportunity-structure template into a references/ file (e.g. RICE.md), keeping SKILL.md as a concise overview with a one-level link.

Tighten the 'Core Principle: Enrichment Analysis > Error Logs' section — the error-rate-vs-failure-rate point is restated in 2e; state it once and reference forward.

Consider collapsing the per-phase tool-call budgets and param tables into a single quick-reference table to reduce repeated procedural prose.

DimensionReasoningScore

Conciseness

Mostly efficient procedural guidance with specific tool calls, budgets, and tables rather than padding; the 'Core Principle' section earns its place by explaining domain-specific enrichment logic Claude would not already know. A few explanatory passages (e.g., restating 'error rate ≠ failure rate' across sections) could be trimmed, so it sits just below the lean-and-efficient anchor.

4 / 5

Actionability

Fully executable: concrete tool calls with explicit parameters ('search' with 'isOfficial: true, sortOrder: "viewCount", limitPerQuery: 15'), call budgets ('10-15 tool calls'), an enriched-field reference table, and a copy-paste-ready opportunity template with a RICE formula and numeric anchors. Matches the fully-executable top anchor.

5 / 5

Workflow Clarity

Six phases are clearly sequenced with explicit validation checkpoints — a RICE quality gate (>= 100), 'Verify currency' deployment checks, multi-source evidence requirements, and a Troubleshooting section for error recovery. Read-only analysis so the destructive-cap does not apply; matches the explicit-validation-and-feedback-loops anchor.

5 / 5

Progressive Disclosure

Well-organized with clear phase headers, tables, and a template block, and no nested/deep references. But it is a ~250-line monolith with no bundle files, and the detailed RICE anchor tables and opportunity-structure template could plausibly live in separate reference files, so it stops just short of the ideal overview-with-one-level-references anchor.

4 / 5

Total

18

/

20

Passed

Description

100%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 exemplary: third-person voice, concrete capabilities across three named data sources, and an explicit 'Use when' clause with seven natural trigger phrases. It cleanly answers both what the skill does and when to invoke it with minimal conflict risk.

DimensionReasoningScore

Specificity

Lists multiple concrete actions across named data sources — 'cross-referencing Amplitude analytics (dashboards, charts, funnels, feedback, AI agent analytics), optionally Datadog (errors, latency, stack traces), and optionally Slack' plus 'Identifies what's broken, what's working, and what to do about it — with root causes'. This matches the comprehensive-coverage anchor; nothing above it exists.

5 / 5

Completeness

Clearly answers 'what' (diagnoses product health by cross-referencing sources, identifies broken/working/actions with root causes) and explicitly answers 'when' via a 'Use when…' clause with concrete trigger phrases. Hits the top anchor; not 4 because both halves are explicit and specific.

5 / 5

Trigger Term Quality

Provides a comprehensive set of natural phrases users would actually say — 'diagnose my product', 'what's going on', 'product health check', 'what's broken', 'where are users struggling', 'give me a product diagnosis', 'what should I focus on'. Matches the comprehensive-synonyms anchor.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (product health diagnosis via Amplitude/Datadog/Slack) with distinct trigger phrases unlikely to fire for unrelated skills. Matches the clear-niche, minimal-conflict anchor.

5 / 5

Total

20

/

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
amplitude/builder-skills
Reviewed

Table of Contents

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