legout/skills

workflow-from-chats

Extract durable working preferences from recent Cursor chats and convert them into skills, rules, or workflow docs.

查看源码

使用方法

  1. 复制安装命令,在自己的智能体环境安装前先检查源码包。
  2. 从原始文档核对所需工具、凭据和支持的宿主。
  3. 说明输入材料与期望交付物,并对照来源检查输出。
npx skills add https://github.com/legout/skills --skill workflow-from-chats

开始前需了解

  • 安装不包含模型订阅、API 额度或访问文件的权限。
  • 仓库星数和安装量反映活跃情况,不等于独立质量或安全测试。
仓库原始内容

按源仓库内容呈现,保留标题、案例、代码、表格、链接以及原文引用的演示图片。

Workflow From Chats

Infer durable working preferences from recent chats. Do not summarize chats; extract reusable workflow guidance.

Use improve-skill instead when the request is to diagnose or revise one skill from a specific coding-agent session. This skill mines a broader recent corpus for cross-cutting preferences, rules, and workflows.

Scope

  • Default to the last 7 days unless the user asks for a different window.
  • Use the host's supported Cursor chat-history access. If it is unavailable, ask for a user-provided export; do not scrape private application databases.
  • Read parent transcripts and relevant subagent transcripts. Use subagent content as evidence, but cite only parent conversations.
  • Do not expose local transcript paths, secrets, customer data, private chat content, or credentials.

Workflow

  1. State the target workflow or preference surface in one paragraph.
  2. Build an internal transcript inventory: title/topic, parent conversation ID, approximate date, completion state, relevant subagents, and why it may contain preference evidence.
  3. Scan for explicit preferences, corrections, and workflow markers such as "I prefer", "always", "never", "not what I asked", "stop", "review", "PR", "CI", "logs", and "skill".
  4. Extract preference atoms: trigger, workflow step, decision rule, quality bar, stop condition, evidence, and confidence.
  5. Rate confidence as strong, medium, weak, or contradicted.
  6. Cluster by workflow shape rather than transcript: shipping, review, simplification, debugging, capture, communication, delegation, or validation.
  7. Choose the artifact: new skill, skill edit, rule, workflow doc, or no artifact.
  8. Draft only the reusable guidance. Filter anecdotes that will not help future tasks.

Confidence

  • Strong: explicit user preference, workflow-changing correction, repeated parent-chat pattern, or direct request to encode behavior.
  • Medium: accepted workflow, repeated tool/model/validation preference, or subagent consensus that the parent used successfully.
  • Weak: agent-chosen behavior with no user feedback, one ambiguous transcript, or a likely task-specific correction.
  • Contradicted: evidence points in incompatible directions; ask the user before writing files.

Artifact Choice

  • Skill: recurring multi-step workflow with clear triggers.
  • Rule: general behavior that should apply broadly.
  • Workflow doc: useful context that is not reliably triggerable.
  • No artifact: situational, stale, or low-confidence observation.

Output

Return a concise synthesis first:

  • Target workflow.
  • Evidence corpus with parent conversation citations only.
  • Preference profile.
  • Adopt, consider, dismissed.
  • Proposed artifacts.
  • Open questions only if they block writing.

Provenance

Adapted from `cursor/plugins` at commit 93b00b89ef425a9c1bac0d0b317dfc49c930ac99 (MIT).

来自同一仓库

更多 Skills

全部 Skills