# Analyze Performance Call And Learn

Step of the analyze-performance playbook: calls ended tests, scores last week's calls...

- Repository: https://github.com/gooseworks-ai/goose-skills (1276 stars)
- Category: Testing
- License: MIT
- Last updated: 2026-10-27
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add gooseworks-ai/goose-skills --skill analyze-performance-call-and-learn -g -a claude-code -y`
Codex: `npx skills add gooseworks-ai/goose-skills --skill analyze-performance-call-and-learn -g -a codex -y`
Cursor: `npx skills add gooseworks-ai/goose-skills --skill analyze-performance-call-and-learn -g -a cursor -y`
Gemini CLI: `npx skills add gooseworks-ai/goose-skills --skill analyze-performance-call-and-learn -g -a gemini-cli -y`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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 (`brand_knowledge_browse` kind 23 / `brand_knowledge_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.

The rest: https://github.com/gooseworks-ai/goose-skills/blob/HEAD/skills/marketing/analyze-performance-call-and-learn/SKILL.md