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Agent Skill · 研究检索

synthesis-context-lifecycle(综合上下文生命周期)

synthesis-context-lifecycle 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。

synthesis-context-lifecycle 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 63,254。

总安装 318周安装 13GitHub Stars 3下载 102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:synthesis-context-lifecycle(综合上下文生命周期)
来源仓库:https://github.com/rajivpant/synthesis-skills
仓库路径:skills/synthesis-context-lifecycle
安装命令:
npx skills add https://github.com/rajivpant/synthesis-skills --skill synthesis-context-lifecycle
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/rajivpant/synthesis-skills --skill synthesis-context-lifecycle

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

synthesis-context-lifecycle 用于查找、检索和筛选相关信息,适合在关键词或任务场景下快速定位候选结果。适用于 Codex、Claude、Cursor、Gemini CLI 等宿主环境中的研究检索任务。通过 npx skills add 命令从指定 GitHub 仓库安装并使用该技能。

安装前需确认权限范围、维护状态及是否涉及联网或文件操作。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/synthesis-context-lifecycle

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/rajivpant/synthesis-skills --skill synthesis-c…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

synthesis-context-lifecycle 怎么安装?

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

synthesis-context-lifecycle 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。

synthesis-context-lifecycle 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

synthesis-context-lifecycle 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。

synthesis-context-lifecycle 用的人多吗?

公开数据是累计安装 318 次,近一周安装 13 次,下载 102 次,GitHub 3 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。

synthesis-context-lifecycle 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
synthesis-context-lifecycle

综合上下文生命周期 · 本页

3183只读未明确有提醒

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

Context Lifecycle Management

The Problem

AI collaborators start every session with zero context. Their effectiveness depends entirely on the quality of the context they receive. For short-lived projects (2-3 sessions), a single context file works. For long-running projects spanning weeks or months, that file grows unboundedly — combining four types of information with fundamentally different lifecycles:

Information typeAccess patternGrowth patternIdeal treatment
Working memory (current state, active tasks)Every sessionConstantKeep lean, refresh often
Episodic memory (session logs)Rarely after 1 weekUnbounded appendArchive monthly
Semantic memory (stable facts, reference)Most sessionsSlow, update-in-placeSeparate file
Completed work recordsAlmost neverUnbounded appendDelete after archiving

Combining all four in one file means the file grows linearly with session count, with no mechanism for information to leave. This is the classic hot/warm/cold data problem from database engineering, manifesting in AI context management.


The Architecture

Three Tiers

project/
├── CONTEXT.md      # Working memory (budget: ≤150 lines)
├── REFERENCE.md    # Semantic memory (stable facts, update in place)
├── sessions/       # Episodic memory (archived session logs)
│   └── YYYY-MM.md  # Monthly files
└── [other files]   # Transcripts, artifacts, etc.

This maps to both cognitive science and systems engineering:

Human memoryCPU cacheSynthesis equivalentProperties
Working memoryL1 cacheCONTEXT.mdSmall capacity, constantly refreshed, always loaded
Semantic memoryL2 cacheREFERENCE.mdFacts and relationships, updated in place, loaded on demand
Episodic memoryL3 cachesessions/Chronological events, append-only, searched when needed
Procedural memoryFirmwareCLAUDE.md + lessons/How to do things, rules, patterns

These are design principles, not metaphors. Each memory type has different storage, retrieval, and maintenance characteristics.

CONTEXT.md — Working Memory

Purpose: Everything the AI collaborator needs to be effective in THIS session.

Budget: ≤150 lines (hard). For completed projects: ≤80 lines.

Contains ONLY:

  • Phase/status header (~5 lines)
  • Current state (~15 lines)
  • Active tasks with priorities (~50 lines)
  • Recent session summaries — last 1-2 only (~30 lines)
  • Links to REFERENCE.md and sessions/ (~5 lines)
  • Budget footer (~2 lines)

Does NOT contain:

  • Completed task checklists (archive to sessions/ first, verify, then remove)
  • Session logs older than 1 week (move to sessions/)
  • Stable reference facts (live in REFERENCE.md)
  • Detailed historical narrative (live in session archive)

Template — new project:

# [Project Name] — Working Context

**Phase:** Initial
**Status:** [description]
**Last session:** YYYY-MM-DD

---

## Current State

[What exists, what doesn't, starting conditions]

## What's Next

1. [ ] [First task]
2. [ ] [Second task]

---

*This file follows the Tiered Context Architecture. Budget: ≤150 lines.*

Template — mature project:

# [Project Name] — Working Context

**Phase:** [Current phase]
**Status:** [Active/Paused]
**Last session:** YYYY-MM-DD

For stable reference facts: see [REFERENCE.md](REFERENCE.md)
For session history: see [sessions/](sessions/)

---

## Current State

- **Production:** [version, deployment status]
- **Blockers:** [if any]

## What's Next — Prioritized

**High:**
1. [ ] [Task with context]

**Medium:**
2. [ ] [Task]

**Deferred:**
3. [ ] [Task — reason for deferral]

## Recent Session: YYYY-MM-DD

[Summary: what was done, decisions made, outcomes]

---

*This file follows the Tiered Context Architecture. Budget: ≤150 lines.*

Template — completed project:

# [Project Name] — Context

**Status:** Completed
**Completed:** YYYY-MM-DD
**Outcome:** [1-2 sentence summary]

---

## Summary

[What was built/accomplished, 5-10 lines]

## Key Decisions

[Notable decisions that might matter if revisited, 5-10 lines]

---

*Completed project. For historical sessions, see [sessions/](sessions/).*

REFERENCE.md — Semantic Memory

Purpose: Stable facts that don't change session-to-session.

Budget: ≤300 lines (soft). Exceeding 300 lines signals the project scope may be too broad.

Contains:

  • Project overview and goals (if not obvious from name)
  • Team roster with roles
  • URLs, repos, remotes, deployment configuration
  • Architecture decisions and conventions
  • File indexes (transcript logs, artifact locations)
  • Setup and cleanup instructions

Key property: Update IN PLACE, not append. When a team member leaves, update the roster — do not add a dated note. When a URL changes, change the URL. This is a living reference document, not a log.

Template:

# [Project Name] — Reference

Stable facts for this project. Updated in place when facts change.

---

## Quick Reference

| Resource | Location |
|----------|----------|
| [Key URL] | [value] |
| [Key command] | [value] |

## Team

| Name | Role | Notes |
|------|------|-------|
| [Name] | [Role] | [Status] |

## Architecture

[Key decisions, conventions, patterns]

## Related Files

[Index of transcripts, artifacts, external documents]

sessions/ — Episodic Archive

Purpose: Historical record of what happened and when. Rarely read, but searchable when historical context is needed.

Organization: Monthly files named YYYY-MM.md.

Template:

# Session Archive — [Month] [Year]

Archived from CONTEXT.md on YYYY-MM-DD. See REFERENCE.md for stable project facts.

---

### YYYY-MM-DD: [Session title — what was accomplished]

[Summary: 5-15 lines per session. What was done, decisions made, outcomes.]

The Archival Protocol

When to Archive

Archive when ANY of these conditions are true:

  • CONTEXT.md exceeds 120 lines (approaching 150-line budget)
  • Session logs in CONTEXT.md are older than 1 week
  • A project phase transition occurs
  • The user explicitly requests cleanup

Step by Step

  1. Read CONTEXT.md and count lines.
  2. Identify cold content:

- Completed task items - Session summaries older than 1 week - Stable facts that belong in REFERENCE.md - Detailed narratives that belong in sessions/

  1. Create files if needed:

- REFERENCE.md (if stable facts exist and no REFERENCE.md yet) - sessions/ directory - sessions/YYYY-MM.md for the relevant month

  1. Archive FIRST (two-phase commit — write to destination before removing from source):

- Session logs → sessions/YYYY-MM.md (append chronologically) - Stable facts → REFERENCE.md (organize by category) - Completed tasks → sessions/YYYY-MM.md (summarize, then remove from CONTEXT.md)

  1. Verify archives exist — Confirm moved content is present in its destination file.
  2. Only then rewrite CONTEXT.md with archived content removed.
  3. Verify:

- CONTEXT.md ≤150 lines - No information lost (everything archived before removal) - Cross-references updated (CONTEXT.md points to REFERENCE.md and sessions/)

  1. Commit AND push with message: "Maintain context: archive sessions, extract reference facts". Stage only the files this invocation modified — do not git add -A. See the Commit Protocol section below for the full rule.

CRITICAL: Archive FIRST, then delete. NEVER delete content from CONTEXT.md before confirming it exists in sessions/ or REFERENCE.md. Two-phase commit: write to destination, verify, then remove from source.

ALSO CRITICAL: Commit-and-push is part of this protocol, not an afterthought. Every non-trivial context modification (archival or otherwise) must be committed and pushed in the same invocation. See the Commit Protocol section for scoping rules.

Decision Tree: Where Does This Content Belong?

Is this information needed for TODAY's work?
├── Yes → CONTEXT.md
└── No
    ├── Is it a stable fact (team, URL, architecture)?
    │   ├── Yes → REFERENCE.md (update in place)
    │   └── No
    │       ├── Is it a record of what happened during a session?
    │       │   ├── Yes → sessions/YYYY-MM.md
    │       │   └── No
    │       │       └── Is it a reusable lesson?
    │       │           ├── Yes → lessons/
    │       │           └── No → delete it
    └── Exception: completed milestones (≤10 lines) stay in CONTEXT.md

Migration Guide

For Projects Over 500 Lines

Full restructuring. Do NOT mechanically split — each project needs judgment about what is working memory vs reference vs archive.

  1. Read the entire CONTEXT.md
  2. Identify the four content types
  3. Create REFERENCE.md with semantic content
  4. Create sessions/ with episodic content (grouped by month)
  5. Rewrite CONTEXT.md as fresh working memory
  6. Verify nothing was lost

For Projects 150-500 Lines

Moderate restructuring:

  1. Extract obvious semantic content (team, URLs, architecture) → REFERENCE.md
  2. Move session logs → sessions/
  3. Tighten CONTEXT.md to ≤150 lines

For Projects Under 150 Lines

Lightweight touch:

  1. Add budget footer
  2. If >20 lines of reference material exist, consider extracting to REFERENCE.md
  3. If completed, simplify to completion summary format

Project Status Transitions

TransitionCONTEXT.md ActionOther Actions
active → completedRewrite as completion summary (≤80 lines)Simplify REFERENCE.md
active → pausedAdd "Paused State" header with reasonArchive session logs
paused → activeRemove "Paused State" header, refreshUpdate last_session
completed → archivedFreeze all filesSet status in index.yaml
active → spawnedRemove spawned scopeCreate new project

Project Spawning

When a sub-scope exceeds the parent project's boundaries:

  1. Create new project directory
  2. Seed CONTEXT.md with fresh working memory (not a copy)
  3. Add to index.yaml with related: linking to parent
  4. Remove spawned scope from parent's CONTEXT.md
  5. Cross-reference both projects

The test: Would a new team member reading only the parent's CONTEXT.md be confused by the spawned work? If yes, spawn it.


Measuring Context Quality

Quantitative

MetricTarget
CONTEXT.md line count≤150 (active) / ≤80 (completed)
REFERENCE.md line count≤300
Stale session logs (>1 week old in CONTEXT.md)0
Completed tasks remaining in CONTEXT.md0
Budget footer presentYes

Qualitative

After reading CONTEXT.md, the AI collaborator should be able to answer:

  1. What is the current state of this project?
  2. What should I work on next?
  3. What was done in the last session?
  4. Where do I find stable reference information?

If any question cannot be answered from CONTEXT.md alone (with a pointer to REFERENCE.md), the working memory is incomplete.


Context as Infrastructure

In traditional engineering, code is managed as infrastructure — version control, CI/CD, testing, deployment. In synthesis engineering (human-AI collaborative development), there is a third infrastructure layer: context infrastructure — the structured information that enables an AI collaborator to be effective across sessions.

The three infrastructure layers:

  1. Code infrastructure — git, CI/CD, deployment (solved by traditional engineering)
  2. Knowledge infrastructure — lessons, runbooks, compiled knowledge bases (the organizational learning layer)
  3. Context infrastructure — working memory, reference facts, session history (the novel contribution — no equivalent in traditional engineering because human engineers carry context in their heads)

Evolution Stages

  1. Ad hoc — Re-explain everything each session (most AI users today)
  2. Monolithic — Single context file that grows forever (common early approach)
  3. Tiered — Working memory + reference + archive with lifecycle management (this skill)
  4. Compiled — Context automatically assembled from project state, code, and history (future vision)

Stage 3 is the 80/20 solution that makes long-running AI-assisted projects sustainable. Stage 4 is the long-term vision where context at session start is compiled from live project state rather than manually maintained.


Commit Protocol — Not Optional

Context files are only useful if they reach the remote repository. Every invocation that creates or modifies context files (CONTEXT.md, REFERENCE.md, session archives, daily plans, transcripts) must commit and push those changes before the invocation ends.

This is not deferred to "end of day" or "end of session." Treat it as part of the same action: if you wrote to a context file, you also commit and push it. Interactive sessions span multiple turns and may not have a clean "end" — so the commit must happen at the point of modification, not at some later checkpoint that may never arrive.

Scope Rule: Only Commit Repos Touched in This Invocation

Never run workspace-wide commit or push operations. Only commit repos where this specific invocation created or modified files. If the user has other uncommitted work in unrelated repos (personal projects, other workspaces, unrelated branches), leave those alone — they belong to other sessions.

The pattern:

  1. Track which files THIS invocation modified (the agent's own action history is the source of truth).
  2. Group those files by containing repo.
  3. For each containing repo: git add ONLY those specific files (never git add -A or git add.), commit, push.
  4. Never touch repos where you did not create or modify files in this invocation.

Why Point-of-Modification, Not Session-End

Context that exists only on one machine is invisible to the next session on a different machine. The entire point of structured context is cross-session continuity — uncommitted context breaks that guarantee.

Deferring commits to session-end has three failure modes:

  1. Session never ends cleanly. Long-running conversations resume across days; a "session-end" hook may never fire.
  2. Modified ritual modes skip the day-end checklist. Mid-day syncs, vacation/observer catch-ups, and partial rituals update context without running the full day-end sequence. If commit is only in day-end, these updates never ship.
  3. Multiple invocations compound uncommitted work. By the time someone notices, there's a week of context changes stranded on one machine.

Verification

Use synthesis-repo-guard as a detector across the workspace (reports dirty repos) but NEVER as a committer. The commit step must be explicit and per-repo-scoped to this invocation's actual changes. See synthesis-repo-guard for session-end hook integration — that hook should alert and surface dirty repos, not blanket-commit them.

平台分布

Codex

35.19%
按下载量换算36

Claude

31.53%
按下载量换算32

Cursor

19%
按下载量换算19

Gemini CLI

9.7%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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