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Agent Skill · 研究检索

context-recovery(context recovery 搜索)

context-recovery 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。

context-recovery 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,权限为需要联网;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 48,806。

总安装 675周安装 29GitHub Stars 52下载 237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:context-recovery(context recovery 搜索)
来源仓库:https://github.com/aaaaqwq/claude-code-skills
仓库路径:skills/context-recovery
安装命令:
npx skills add https://github.com/aaaaqwq/claude-code-skills --skill context-recovery
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/aaaaqwq/claude-code-skills --skill context-recovery

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。

用于查找、检索和筛选相关信息以恢复工作上下文。适合在会话截断或用户引用先前工作时快速定位候选结果。支持自动触发和手动触发两种模式,适用于多种通信渠道。

安装命令:npx skills add https://github.com/aaaaqwq/claude-code-skills --skill context-recovery 使用前建议确认权限范围和维护状态,注意可能涉及联网操作。

来源仓库内路径:skills/context-recovery

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/aaaaqwq/claude-code-skills --skill context-rec…;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

    它需要需要联网,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

context-recovery 怎么安装?

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

context-recovery 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。

context-recovery 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

context-recovery 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。

context-recovery 用的人多吗?

公开数据是累计安装 675 次,近一周安装 29 次,下载 237 次,GitHub 52 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。

context-recovery 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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。

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。

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
context-recovery

context recovery 搜索 · 本页

67552需要联网未明确未通过

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

Context Recovery

  • Author: Daniel Li
  • Copyright © Daniel Li. All rights reserved.

Automatically recover working context after session compaction or when continuation is implied but context is missing. Works across Discord, Slack, Telegram, Signal, and other supported channels.

Use when: Session starts with truncated context, user references prior work without specifying details, or compaction indicators appear.


Triggers

Automatic Triggers

  • Session begins with a <summary> tag (compaction detected)
  • User message contains compaction indicators: "Summary unavailable", "context limits", "truncated"

Manual Triggers

  • User says "continue", "did this happen?", "where were we?", "what was I working on?"
  • User references "the project", "the PR", "the branch", "the issue" without specifying which
  • User implies prior work exists but context is unclear
  • User asks "do you remember...?" or "we were working on..."

Execution Protocol

Step 1: Detect Active Channel

Extract from runtime context:

  • channel — discord | slack | telegram | signal | etc.
  • channelId — the specific channel/conversation ID
  • threadId — for threaded conversations (Slack, Discord threads)

Step 2: Fetch Channel History (Adaptive Depth)

Initial fetch:

message:read
  channel: <detected-channel>
  channelId: <detected-channel-id>
  limit: 50

Adaptive expansion logic:

  1. Parse timestamps from returned messages
  2. Calculate time span: newest_timestamp - oldest_timestamp
  3. If time span < 2 hours AND message count == limit:

- Fetch additional 50 messages (using before parameter if supported) - Repeat until time span ≥ 2 hours OR total messages ≥ 100

  1. Hard cap: 100 messages maximum (token budget constraint)

Thread-aware recovery (Slack/Discord):

# If threadId is present, fetch thread messages first
message:read
  channel: <detected-channel>
  threadId: <thread-id>
  limit: 50

# Then fetch parent channel for broader context
message:read
  channel: <detected-channel>
  channelId: <parent-channel-id>
  limit: 30

Parse for:

  • Recent user requests (what was asked)
  • Recent assistant responses (what was done)
  • URLs, file paths, branch names, PR numbers
  • Incomplete actions (promises made but not fulfilled)
  • Project identifiers and working directories

Step 3: Fetch Session Logs (if available)

# Find most recent session files for this agent
SESSION_DIR=$(ls -d ~/.clawdbot-*/agents/*/sessions 2>/dev/null | head -1)
SESSIONS=$(ls -t "$SESSION_DIR"/*.jsonl 2>/dev/null | head -3)

for SESSION in $SESSIONS; do
  echo "=== Session: $SESSION ==="

  # Extract user requests
  jq -r 'select(.message.role == "user") | .message.content[0].text // empty' "$SESSION" | tail -20

  # Extract assistant actions (look for tool calls and responses)
  jq -r 'select(.message.role == "assistant") | .message.content[]? | select(.type == "text") | .text // empty' "$SESSION" | tail -50
done

Step 4: Check Shared Memory

# Extract keywords from channel history (project names, PR numbers, branch names)
# Search memory for relevant entries
grep -ri "<keyword>" ~/clawd-*/memory/ 2>/dev/null | head -10

# Check for recent daily logs
ls -t ~/clawd-*/memory/202*.md 2>/dev/null | head -3 | xargs grep -l "<keyword>" 2>/dev/null

Step 5: Synthesize Context

Compile a structured summary:

## Recovered Context

**Channel:** #<channel-name> (<platform>)
**Time Range:** <oldest-message> to <newest-message>
**Messages Analyzed:** <count>

### Active Project/Task
- **Repository:** <repo-name>
- **Branch:** <branch-name>
- **PR:** #<number> — <title>

### Recent Work Timeline
1. [<timestamp>] <action/request>
2. [<timestamp>] <action/request>
3. [<timestamp>] <action/request>

### Pending/Incomplete Actions
- ⏳ "<quoted incomplete action>"
- ⏳ "<another incomplete item>"

### Key References
| Type | Value |
|------|-------|
| PR | #<number> |
| Branch | <name> |
| Files | <paths> |
| URLs | <links> |

### Last User Request
> "<quoted request that may not have been completed>"

### Confidence Level
- Channel context: <high/medium/low>
- Session logs: <available/partial/unavailable>
- Memory entries: <found/none>

Step 6: Cache Recovered Context

Persist to memory for future reference:

# Write to daily memory file
MEMORY_FILE=~/clawd-*/memory/$(date +%Y-%m-%d).md

cat >> "$MEMORY_FILE" << EOF

## Context Recovery — $(date +%H:%M)

**Channel:** #<channel-name>
**Recovered context for:** <project/task summary>

### Key State
- <bullet points of critical context>

### Pending Items
- <incomplete actions>

EOF

This ensures context survives future compactions.

Step 7: Respond with Context

Present the recovered context, then prompt:

"Context recovered. Your last request was [X]. This action [completed/did not complete]. Shall I [continue/retry/clarify]?"

Channel-Specific Notes

Discord

  • Use channelId from the incoming message metadata
  • Guild channels have full history access
  • Thread recovery: check for threadId in message metadata
  • DMs may have limited history

Slack

  • Use channel parameter with Slack channel ID
  • Thread context requires threadId — always check for thread context first
  • Parent channel fetch provides surrounding conversation context
  • May need workspace-level permissions for full history

Telegram / Signal / Others

  • Same message:read interface
  • History depth may vary by platform
  • Group vs. DM context may differ

Constraints

  • MANDATORY: Execute this protocol before responding "insufficient data" or asking clarifying questions when context appears missing
  • Adaptive depth: start with 50, expand to 100 max
  • Time-based goal: capture at least 2 hours of context when possible
  • Session log extraction: last 3 session files maximum
  • Memory cache: append to daily file, do not overwrite
  • If recovery fails, state what was attempted and what data sources were unavailable

Auto-Trigger Detection

At session start, scan for compaction indicators:

# Pseudocode for trigger detection
if message contains "<summary>":
    trigger_context_recovery()
elif message contains any of ["Summary unavailable", "context limits", "truncated", "compacted"]:
    trigger_context_recovery()
elif message matches continuation_patterns:
    # "continue", "where were we", "did this happen", etc.
    trigger_context_recovery()

Do not wait for user to ask — if compaction is detected, proactively recover and present context.


Example

Scenario: Session starts with compaction summary

User message: "<summary>Summary unavailable due to context limits...</summary>"
User message: "did this happen?"

Agent executes:

  1. Detects compaction via <summary> tag
  2. message:read channel=discord channelId=1460342116516364533 limit=50
  3. Calculates time span: 2.5 hours — sufficient, no expansion needed
  4. Parses messages, finds last action: "Now let me extract the key orchestration decisions..."
  5. Checks session logs — extracts recent user requests
  6. Writes context summary to memory/YYYY-MM-DD.md
  7. Responds:
"Context recovered. Your last request was to extract orchestration decisions from Nexus archived sessions for PR #137 (Skills & Agents System). This action did not complete before context was truncated. Recovered state: - PR #137: 15,053 additions, 91 files, CONFLICTING merge state - Spec location: content/spec-skills-agents-system.md (2,067 lines) - Nexus sessions: ~/.clawdbot-duke-leto/archive/nexus-sessions/ (96 files) Shall I proceed with the extraction?"

平台分布

Codex

36.48%
按下载量换算86

Claude

28.45%
按下载量换算67

Cursor

17.87%
按下载量换算42

Gemini CLI

8.95%
按下载量换算21

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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