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AD product-analytics-and-measurement

Define observable, governed evidence for product outcomes through metric trees, event/tracking plans, instrumentation QA, and measurement governance. Use when defining product metrics, designing tracking plans, building event taxonomies, running product funnels or cohort analysis, setting up dashboard contracts, or auditing instrumentation quality. Do NOT use for statistical inference or experiment design (route to data-scientist), data pipeline implementation (route to data-engineering), data architecture decisions (route to data-architect), observability or infrastructure monitoring (route to site-reliability-engineering), or general business intelligence and dashboard building (route to BI tooling).

magnus919/agent-skills Agent Skills author: magnus919 MIT 8 files body ≈ 2 513 tokens Open the sourcegithub.com↗ analyzed 26 h ago

Define observable, governed evidence for product outcomes through metric trees, event/tracking plans, instrumentation QA, and measurement governance.

As a process D 45/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureData and analyticstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
D
45/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 45/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 85Steps. 54 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2513 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low No test case covers injection arriving through data

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 711: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 54 items
    • +4Reference files are cited in the instructions (2 of 2)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.