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/analyze-performance-call-and-learn

Step of the analyze-performance playbook: calls ended tests, scores last week's calls, writes this week's kill/scale/hold calls and drafts learnings for the brand brain. Run analyze-performance instead; not for direct use.

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$ npx -y skills add gooseworks-ai/goose-skills --skill analyze-performance-call-and-learn --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/analyze-performance-call-and-learn

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Step of the analyze-performance playbook: calls ended tests, scores last week's calls, writes this week's kill/scale/hold calls and drafts learnings for the brand brain. Run analyze-performance instead; not for direct use.

SKILL.md

analyze-performance-call-and-learn.SKILL.md
name: analyze-performance-call-and-learn
description: >-
  Step of the analyze-performance playbook: calls ended tests, scores last week's calls, writes this
  week's kill/scale/hold calls and drafts learnings for the brand brain. Run analyze-performance
  instead; not for direct use.
version: 0.1.1

Test calls, calls and learnings (atom)

**Load:** `../analyze-performance/references/tests-and-learnings.md`.

**Inputs:** the brief's test plan; this week's results; last week's report (its calls); the change log (what was actually done); the learnings and readouts (`brain_map` kind 23 / `brain_search` where listed; else `knowledge_search source_types: ["learning"]`, `brand_read` learnings); the paid run's newest run record.

**Do:** 1. Call each ended test Win / Loss / Unclear against its written criteria with the brief's action; state how sure (confirmed / likely / first look). 2. Score last week's recommendations through run-memory's outcome check (`../analyze-performance/references/run-memory.md`): what was actually done (the `### YYYY-MM-DD — <title>` blocks in `decisions.md`, run-paid-campaigns' run record), and what happened — for a money move, the paid run's verdict plus the store-joined check over the same window; right or wrong. 3. Write this week's calls (kill / scale / hold, Google negatives) with object, why, how sure and "would change if"; first look → "watch". 4. Check fired replan triggers against the brief's thresholds. 5. Draft ≤ 3 learnings as one-line rules (template in the reference); only ended tests, 2+ week patterns that clear the test n floors on the referee-joined number, confirmed surprises or failures; each shows what it would retire. 6. Readouts: each Win / Loss → kind 23 `experiment_readout.<campaign-slug>-t<n>` through save-learning's readout path, only with the marketer present; scheduled runs mark it `readout to save`. 7. After the marketer's reply to the decisions block: write the kept learnings through the `save-learning` skill (`../save-learning/SKILL.md`) — one idea per learning, `[learning YYYY-MM-DD]` (+ ` A-NN`), their exact words as the statement, own superseded facts retired in the same call; read back, compare, report which landed. Without their words, write nothing.

**Output:** the report's Tests, Calls, Learnings-proposed sections; the learnings rows written after the marketer has seen them.

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