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decision-capture决策捕捉

Agent Skill

decision-capture 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

98

周安装

4

GitHub Stars

公开资料未说明

下载量

31
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:decision-capture(决策捕捉)
来源仓库:https://github.com/wellapp-ai/well
仓库路径:skills/decision-capture
安装命令:
npx skills add wellapp-ai/well --skill "decision-capture"
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

AgentSkills.tonpx skills
npx skills add wellapp-ai/well --skill "decision-capture"

简介

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。

  • 适用于决策捕捉相关的任务场景。
  • 可结合来源仓库和原始 README 核验具体用法。
  • 安装前建议确认权限范围和维护状态。
  • decision-capture 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
decision-capture
description
Capture patine (decision wisdom) at Gates when KO or challenge occurs

Decision Capture Skill

Lightweight skill for capturing the "patine" - the accumulated wisdom of why decisions were made and alternatives rejected. Triggered at Gates when human provides KO or challenges a proposal.

When to Use

  • At any Gate when human provides KO
  • When human challenges or rejects a proposal
  • When debug skill finds a pattern worth remembering
  • When significant technical decision is made

Trigger Conditions

TriggerContextType
Gate 1 KOWireframe rejectedDecision
Gate 2 KOScope option rejectedDecision
Gate 3 KO/BLOCKPhasing challengedDecision
Gate 4 KOTechnical approach rejectedDecision
Gate 5 ChangesPR review feedbackDecision
Debug PatternRecurring issue foundKaizen
DIG 3+ timesSame wireframe refined repeatedlyKaizen
Same error fixed 2+ timesFix pattern emergedKaizen
User corrects assumptionAI was wrong about somethingHansei
Takt warning exceededPhase took longer than targetHansei
Jidoka Tier 2/3Escalation to human requiredHansei

Phase 1: Detect Decision Type

Categorize the decision:

TypeSignalExample
TechnicalCode, architecture, library"Don't use GraphQL subscriptions"
UXInteraction, visual, flow"Sidebar navigation, not top nav"
ProcessWorkflow, phasing, priority"Ship auth before tables"

Phase 2: Prompt for Rationale

Ask for brief rationale (keep it light):

I'll note this decision for future reference.

**In one sentence, why this decision?**

Examples:
- "We tried X in 2024, broke production"
- "Users missed this in testing"
- "Conflicts with our caching strategy"

(Press Enter to skip if you prefer not to explain)

If human declines: Record decision without rationale (still valuable).

Phase 3: Attribute

Capture metadata:

FieldSource
WhoCurrent user (from context)
WhenCurrent date
DomainFrom branch name or changed files
GateWhich Gate triggered capture
Related TaskNotion task ID if available

Phase 4: Store

Layer 1: Notion (Default - Always)

Use Notion MCP to create record:

API-create-page:
  parent: { database_id: "[DECISION_PATINE_DB_ID]" }
  properties:
    Title: { title: [{ text: { content: "[Decision summary]" }}]}
    Domain: { select: { name: "[domain]" }}
    Type: { select: { name: "[Technical/UX/Process]" }}
    Decision: { rich_text: [{ text: { content: "[What we decided]" }}]}
    Rationale: { rich_text: [{ text: { content: "[Why]" }}]}
    Rejected: { rich_text: [{ text: { content: "[What we didn't do and why]" }}]}
    Impact: { select: { name: "[Low/Medium/High]" }}
    Gate: { select: { name: "[Gate 1/2/3/4/5/Debug]" }}

Layer 2: ADR File (If High Impact)

If Impact = High or Type = Technical with cross-domain effect:

  1. Get next ADR number: ls docs/decisions/ | wc -l
  2. Create file: /docs/decisions/NNN-[slug].md
  3. Use ADR template

ADR Template:

# ADR-[NNN]: [Title]

**Date**: [YYYY-MM-DD]
**Status**: Accepted
**Domain**: [domain]
**Captured at**: Gate [N]

## Context

[1-2 sentences: What problem were we solving?]

## Decision

[What we chose to do]

## Rationale

[Why this approach - the positive case]

## Rejected Alternatives

### [Alternative Name]
**Why not**: [Reason]

## Consequences

- [Trade-off 1]
- [Trade-off 2]

## References

- Notion: [link to Decision Patine record]
- Task: [link to related task if applicable]

Layer 3: Inline Comment (If Micro/Code-Specific)

For small code-level decisions during implementation:

// ADR: [Brief decision]. [Why not alternative]. —@[initials] [YYYY-MM]

Example:

// ADR: No useMemo here - profiling showed <1ms gain, adds complexity. —@mc 2026-01

Phase 5: Confirm

Output confirmation:

**Noted:** [Decision summary]

Stored in Decision Patine database.
[If ADR created: Created ADR-[NNN] in /docs/decisions/]

Continuing with workflow...

Kaizen/Hansei Capture (NEW)

Automatic learning capture without user prompts. These triggers capture patterns and reflections silently.

Automatic Triggers

These captures happen automatically without prompting user:

Kaizen (pattern emerged):

  • Wireframe DIG'd 3+ times → Capture the pattern that emerged
  • Same error fixed 2+ times → Capture the fix pattern
  • Repeated code pattern → Capture abstraction opportunity

Hansei (reflection):

  • User corrects AI assumption → Capture what was wrong
  • Phase exceeded takt warning → Capture why it took longer
  • Jidoka escalation → Capture what blocked progress

Kaizen Format

Type: KAIZEN
Source: [Phase] [Loop/Commit]
Learning: "[What pattern emerged]"
Category: [UX_PATTERN | TECHNICAL | PROCESS]
Impact: LOW | MEDIUM | HIGH

Example:

Type: KAIZEN
Source: DIVERGE Loop 3
Learning: "Invite modals benefit from email preview side panel"
Category: UX_PATTERN
Impact: MEDIUM

Hansei Format

Type: HANSEI
Source: [Phase] [Loop/Commit]
Learning: "[What we learned from the mistake/delay]"
Category: [ASSUMPTION | COMPLEXITY | PROCESS]
Impact: LOW | MEDIUM | HIGH

Example:

Type: HANSEI
Source: CONVERGE
Learning: "Original scope too ambitious - exceeded 40min takt warning"
Category: COMPLEXITY
Impact: LOW

Silent Capture Rules

  • Do NOT prompt user for rationale on Kaizen/Hansei triggers
  • Capture automatically based on observed patterns
  • Include in session-journal sync at end of session
  • Only HIGH impact Kaizen/Hansei create immediate ADR files

Session Aggregation

Instead of immediately creating Notion entries for each:

  1. Collect Kaizen/Hansei entries in memory during session
  2. Aggregate in session-journal sync at end (Phase 5 of notion-sync)
  3. Only HIGH impact items create immediate ADR files

Querying Patine

Before proposing new patterns, query existing decisions:

API-query-database:
  database_id: "[DECISION_PATINE_DB_ID]"
  filter:
    property: "Domain"
    select:
      equals: "[current domain]"

Use results to:

  1. Avoid re-proposing rejected alternatives
  2. Understand existing constraints
  3. Reference past decisions in new proposals

Anti-Patterns

DON'T:

  • Require rationale for every micro-decision
  • Create ADR files for non-architectural choices
  • Capture decisions that are already in code comments
  • Ask "why" more than once if human declines

DO:

  • Capture at the moment of friction (KO, challenge)
  • Accept "we tried this before, it failed" as valid rationale
  • Keep entries scannable (1-2 sentences)
  • Link to evidence when available (PRs, issues, metrics)

Integration

This skill is invoked by:

  • ask.mdc - Gates 1, 2, 3 on KO
  • plan.mdc - Gate 4 on option rejection
  • push-pr.mdc - Gate 5 on changes requested
  • debug - When pattern worth remembering is found

Notion Database Schema

Database: Decision Patine

PropertyTypeRequired
TitleTitleYes
DomainSelectYes
TypeSelectYes
DecisionRich TextYes
RationaleRich TextNo
RejectedRich TextNo
Challenged ByPersonNo
DateDateYes
ImpactSelectYes
GateSelectNo
Related TaskRelationNo
ADR FileURLNo

Invocation

Invoked automatically at Gates on KO, or manually with "use decision-capture skill".

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

47.88%
按下载量换算15

Claude Code

27.9%
按下载量换算9

Gemini CLI

13.43%
按下载量换算4

安全审计

暂无安全审计结果可展示。

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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