Agent Skillssediman-agent/OpenSkynet › workflow-from-chats

workflow-from-chats

GitHub

从近期对话中提取持久工作偏好,转化为技能、规则或工作流文档。用于学习用户习惯、挖掘反馈及个性化Agent指导,不总结聊天内容。

skills/cursor_plugins/cursor-team-kit/skills/workflow-from-chats/SKILL.md sediman-agent/OpenSkynet

Trigger Scenarios

要求学习偏好 挖掘反馈 个性化工作流 生成团队或特定人员Agent指导

Install

npx skills add sediman-agent/OpenSkynet --skill workflow-from-chats -g -y
More Options

Non-standard path

npx skills add https://github.com/sediman-agent/OpenSkynet/tree/main/skills/cursor_plugins/cursor-team-kit/skills/workflow-from-chats -g -y

Use without installing

npx skills use sediman-agent/OpenSkynet@workflow-from-chats

指定 Agent (Claude Code)

npx skills add sediman-agent/OpenSkynet --skill workflow-from-chats -a claude-code -g -y

安装 repo 全部 skill

npx skills add sediman-agent/OpenSkynet --all -g -y

预览 repo 内 skill

npx skills add sediman-agent/OpenSkynet --list

SKILL.md

Frontmatter
{
    "name": "workflow-from-chats",
    "description": "Extract durable working preferences from recent Cursor chats and convert them into skills, rules, or workflow docs. Use when asked to learn preferences, mine feedback, personalize workflows, or generate team\/person-specific agent guidance."
}

Workflow From Chats

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

Scope

  • Default to the last 7 days unless the user asks for a different window.
  • 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.

Version History

  • c9d8953 Current 2026-07-05 19:52

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Metadata

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Version
c9d8953
Hash
b5681763
Indexed
2026-07-05 19:52

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