Agent Skills › gooseworks-ai/goose-skills › analyze-performance-call-and-learn

analyze-performance-call-and-learn

GitHub

执行性能分析流程中的测试评估环节,判定测试结果,评分上周建议,撰写本周决策及学习总结,并与营销人员协作沉淀知识。

skills/marketing/analyze-performance-call-and-learn/SKILL.md gooseworks-ai/goose-skills

触发场景

需要评估已结束的性能测试并生成结果报告 需要从历史运行数据中提取经验教训并更新知识库

安装

npx skills add gooseworks-ai/goose-skills --skill analyze-performance-call-and-learn -g -y
更多选项

非标准路径

npx skills add https://github.com/gooseworks-ai/goose-skills/tree/main/skills/marketing/analyze-performance-call-and-learn -g -y

不安装直接使用

npx skills use gooseworks-ai/goose-skills@analyze-performance-call-and-learn

指定 Agent (Claude Code)

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

安装 repo 全部 skill

npx skills add gooseworks-ai/goose-skills --all -g -y

预览 repo 内 skill

npx skills add gooseworks-ai/goose-skills --list

SKILL.md

Frontmatter
{
    "name": "analyze-performance-call-and-learn",
    "version": "0.1.2",
    "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."
}

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.

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

版本历史

  • 1add09b 当前 2026-11-02 21:49

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元信息

文件数
0
版本
1add09b
Hash
12291b50
收录时间
2026-11-02 21:49

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