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Agent Skill · 开发

session-reflection(会话反思)

session-reflection 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

session-reflection 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 10,033。

总安装 2,444周安装 105GitHub Stars 2下载 857核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:session-reflection(会话反思)
来源仓库:https://github.com/jwilger/agent-skills
仓库路径:skills/session-reflection
安装命令:
npx skills add https://github.com/jwilger/agent-skills --skill session-reflection
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。

skills.shnpx skills
npx skills add https://github.com/jwilger/agent-skills --skill session-reflection

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

session-reflection 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。它能自动提取仓库元数据、变更摘要和协作动态,便于跟踪项目进展。

安装命令为 npx skills add https://github.com/jwilger/agent-skills --skill session-reflection,需确认权限范围和维护状态。使用前建议核验是否会触发联网、命令执行或文件读写,并参考原始 README 了解具体用法。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/session-reflection

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/jwilger/agent-skills --skill session-reflectio…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

session-reflection 怎么安装?

在终端执行 npx skills add https://github.com/jwilger/agent-skills --skill session-reflection。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

session-reflection 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

session-reflection 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/jwilger/agent-skills --skill session-reflection 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

session-reflection 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

session-reflection 用的人多吗?

公开数据是累计安装 2,444 次,近一周安装 105 次,下载 857 次,GitHub 2 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

session-reflection 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
session-reflection

会话反思 · 本页

2,4442执行命令未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Session Reflection

Value: Feedback -- every user intervention is a signal that the system prompt is incomplete. Turning corrections into durable instructions creates compound improvement across sessions.

Purpose

Teaches agents to analyze session history for recurring corrections, generate project-specific instructions that prevent known failure modes, and maintain working state that survives context compaction and crashes. Transforms reactive corrections into proactive prevention.

Practices

Reflection Triggers

Reflect after: milestones (PR merged, feature complete), 3+ repeated corrections from the user, session restart or crash recovery, every 5 completed tasks, and on explicit user request. Do not wait for a "good time" -- reflect when triggered.

Analyze Session History

Examine conversation history, git log, memory files, WORKING_STATE.md, and session logs. Categorize each user intervention into one of five types:

  • Correction: Agent did the wrong thing (instruction gap)
  • Repetition: Agent was told the same thing again (emphasis gap)
  • Role Redirect: Agent stepped outside its role (boundary gap)
  • Frustration Escalation: User became more forceful (decay problem)
  • Workaround: User did it themselves (skill gap)

See references/analysis-framework.md for detailed categorization and prioritization.

Generate or Refine Project Instructions

Route project-specific directives into the appropriate instruction file based on content type. There is no separate system prompt file — everything flows from CLAUDE.md → AGENTS.md →.team/coordinator-instructions.md:

  • CLAUDE.md — Session management (startup procedure, compaction recovery, state tracking), harness-specific configuration
  • AGENTS.md — General project rules, dos/don'ts, coding conventions, workflow configuration
  • .team/coordinator-instructions.md — Coordinator/pipeline-controller role distinctions, build pre-flight gates, spawn discipline, domain review checklists

Refinement rules: add new items for new gaps, promote advisory to structural when gaps recur, rewrite ambiguous items for clarity. Never remove items until the gap is confirmed solved across 3+ sessions. See references/system-prompt-patterns.md for writing patterns and references/launcher-templates.md for harness-specific file routing.

Self-Reminder Protocol

See CONSTRAINT-RESOLUTION.md in the template directory for the consolidated self-reminder protocol (frequency, combined re-read list, and post-compaction rules).

Working State Persistence

Maintain WORKING_STATE.md as insurance against context compaction and crashes. Update after every significant state change. Always read before acting after any interruption. Location: .factory/WORKING_STATE.md (pipeline) or project root (standalone). See references/working-state-schema.md for format.

Do:

  • Update on task start, phase change, decision made, blocker encountered
  • Overwrite with current state (not append)
  • Read FIRST after any interruption

Do not:

  • Guess state from memory after a compaction or restart
  • Use as a journal -- keep it concise and current
  • Skip updates because "nothing important changed"

Post-Session Learning Loop

At session end: identify patterns from this session, update memory files, refine project instructions if triggers were hit, archive working state.

The loop closes when the same category of intervention stops recurring. If an intervention category persists across 3+ sessions after instruction refinement, escalate: the gap may require a new skill or a structural change to the workflow.

Enforcement Note

Advisory in all modes. Reflection triggers and instruction generation are self-enforced.

Hard constraints:

  • Mandatory after context compaction: [H] -- context compaction destroys implicit state; re-reading is not optional.

Constraints

  • "3+ repeated corrections": A "repeated correction" means the user corrected the same underlying behavior multiple times. Different phrasings of the same correction count as one correction repeated. Different corrections in the same category (e.g., two different formatting preferences) count separately.
  • "Never remove instructions until confirmed solved across 3+ sessions": This prevents premature cleanup. An instruction that hasn't triggered in 3 sessions might be preventing the problem -- that's success, not staleness. Remove only when you have positive evidence the underlying behavior is fixed (e.g., the code pattern that caused the issue no longer exists).

Verification

After completing work guided by this skill, verify:

  • Reflection performed at every trigger point (milestone, repeated correction, restart)
  • User interventions categorized using the five-type taxonomy
  • Project instructions refined with structural (not just advisory) language
  • Directives routed to correct file (CLAUDE.md / AGENTS.md / coordinator-instructions.md)
  • Self-reminder protocol followed (state re-read every 5-10 messages)
  • WORKING_STATE.md current and accurate
  • State re-read after every context compaction (not guessed)

If any criterion is not met, revisit the relevant practice before proceeding.

Dependencies

This skill works standalone. For enhanced workflows, it integrates with:

  • memory-protocol: Persistent storage for session learnings and working state
  • agent-coordination: Coordination patterns referenced in instruction generation
  • pipeline: Pipeline controller benefits from self-reminder and crash recovery
  • ensemble-team: Team retrospectives feed into session reflection analysis

Missing a dependency? Install with:

npx skills add jwilger/agent-skills --skill memory-protocol

平台分布

Codex

34.93%
按下载量换算299

Claude

27.71%
按下载量换算237

Cursor

19.83%
按下载量换算170

Gemini CLI

9.72%
按下载量换算83

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/jwilger/agent-skills --skill session-reflection 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。目前只有一个公开来源,建议同时核对源仓库。

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