CtrlK
BlogDocsLog inGet started
Tessl Logo

product-analytics-setup

How to actually instrument product analytics correctly. Event taxonomy, property design, naming conventions, schema versioning, identity stitching, funnel design, retention cohorts, North Star metric selection, dashboard hygiene, instrumentation debt, and the failure modes that produce data nobody trusts. Triggers on product analytics setup, event taxonomy, tracking plan, instrumentation, schema versioning, North Star metric, retention cohorts, funnel design, naming conventions, instrument new feature, audit existing analytics, dashboard reconciliation, instrumentation debt, Mixpanel setup, Amplitude setup, PostHog setup, warehouse-native analytics. Also triggers when the team has data but cannot trust it, or when designing instrumentation for a new feature, or when auditing an existing setup that has drifted.

67

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

65%

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 well-organized, actionably specific instruction skill with excellent domain heuristics and a clean reference architecture, but it is prose-verbose, lacks explicit validation feedback loops for migration/audit workflows, and contains one broken inline reference to a missing bundle file.

Suggestions

Tighten conciseness: remove concept-explainer one-liners Claude already knows (e.g., "Retention is repeat behavior over time", "Funnels measure progression through a sequence", "A cohort is a group of users…") and trim the narrative intro; lean on the specific heuristics that earn their tokens.

Add explicit validation/verification checkpoints to the schema-versioning and instrumentation-audit workflows (e.g., a "validate against the data contract before retiring v1" step and a verify-then-proceed feedback loop) so batch/migration operations meet the workflow-clarity bar.

Fix the broken progressive-disclosure link: either add the missing `references/retention-measurement-patterns.md` to the bundle or remove/redirect the inline reference in the retention section so every referenced path resolves.

DimensionReasoningScore

Conciseness

The body is a ~300-line prose document that, while dense with non-obvious domain heuristics ("Thirty to fifty events is the sweet spot", "Bracket retention over N-day for stability"), also explains concepts Claude already knows ("Retention is repeat behavior over time", "Funnels measure progression through a sequence", "A cohort is a group of users sharing an attribute or behavior") and opens with narrative setup. It is not a 3 because it is long and includes framing explanations that could be trimmed; it is not a 1 because there is no generic filler padding and most tokens carry specific domain judgment.

2 / 3

Actionability

Concrete, specific, actionable guidance throughout — "Past tense, action-oriented", "Object-action format", "snake_case", "Append `_v2` to events when semantics change", "Keep old events firing during the transition (90 days is typical)" — with concrete examples (`checkout_completed`, `subscription_tier`, NSMs like Figma's weekly active editors). It is not a 2 because guidance is complete and specific rather than vague or pseudocode; per the scoring notes, absence of code in an instruction-only skill is not penalized when guidance is this actionable.

3 / 3

Workflow Clarity

A clear sequence exists ("The instrumentation hierarchy" bottom-up stack and the numbered "12 considerations" framework), but validation/verification checkpoints are implicit or missing — the schema-migration pattern (fire both versions, migrate dashboards, deprecate v1) has no explicit "verify then proceed" step. It is not a 3 because explicit validation steps and error-recovery feedback loops are absent for batch/migration operations, which the rubric caps at 2; it is not a 1 because the multi-step sequence is clearly laid out.

2 / 3

Progressive Disclosure

Structure is strong — an overview body, inline "Detail in [`references/…`]" pointers per section, and a dedicated "Reference files" index — but the body references `references/retention-measurement-patterns.md` (retention section) which is NOT present in the bundle, so navigation to that detail fails. It is not a 3 because, scored against the actual bundle structure as the rubric directs, one referenced path is broken; it is not a 1 because references are one level deep, clearly signaled, and the index itself lists only the 9 files that actually exist.

2 / 3

Total

9

/

12

Passed

Description

100%

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 comprehensive and well-structured: a dense list of concrete capabilities followed by explicit trigger terms including tool names and natural user phrasings. It answers both what and when clearly and occupies a distinct niche.

DimensionReasoningScore

Specificity

Lists many concrete capabilities in one breath — "Event taxonomy, property design, naming conventions, schema versioning, identity stitching, funnel design, retention cohorts, North Star metric selection, dashboard hygiene, instrumentation debt" — matching the anchor for multiple specific concrete actions. It is not a 2 because the catalog is comprehensive rather than naming only a domain and a few actions; voice is third person ("How to actually instrument…"), so no specificity penalty applies.

3 / 3

Completeness

Explicitly answers both what ("How to actually instrument product analytics correctly…") and when via an explicit "Triggers on…" / "Also triggers when…" clause, satisfying the anchor for both what AND when with explicit triggers. It is not a 2 because the trigger guidance is explicit rather than merely implied.

3 / 3

Trigger Term Quality

Extensive natural-term coverage — "product analytics setup, event taxonomy, tracking plan, instrumentation, Mixpanel setup, Amplitude setup, PostHog setup, warehouse-native analytics" plus the colloquial "team has data but cannot trust it" — terms a user would actually say. It is not a 2 because common variations and tool names are well covered, not just a single keyword.

3 / 3

Distinctiveness Conflict Risk

Triggers are specific to instrumentation execution (tracking plan, schema versioning, Mixpanel/Amplitude/PostHog setup, instrumentation debt), carving a clear niche unlikely to fire for strategy or experimentation skills. It is not a 2 because the trigger set is distinctive and tool/event-instrumentation specific rather than a generic overlap-prone phrase.

3 / 3

Total

12

/

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

frontmatter_unknown_keys

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

Warning

relative_links

Relative link issues: 1 missing

Warning

referenced_paths_exist

Referenced path issues: 2 missing

Warning

Total

13

/

16

Passed

Repository
rampstackco/claude-skills
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.