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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,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 49,373。

总安装 654周安装 27GitHub Stars 232下载 214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/jdrhyne/agent-skills
仓库路径:skills/context-recovery
安装命令:
npx skills add https://github.com/jdrhyne/agent-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/jdrhyne/agent-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。context-recovery 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适合在关键词搜索或任务场景中快速定位候选结果。使用时需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。注意是否会触发联网、命令执行或文件读写操作。

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/jdrhyne/agent-skills --skill context-recovery;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/jdrhyne/agent-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 654 次,近一周安装 27 次,下载 214 次,GitHub 232 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。

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

654232需要联网未明确有提醒

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 Recovery

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..."

Recovery Workflow

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: Read 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:

- Read an 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, read thread messages first
message:read
  channel: <detected-channel>
  threadId: <thread-id>
  limit: 50

# Then read the 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: Read 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.

Safety Boundaries

  • Do not scan unrelated channels, projects, or workspaces when the active thread already gives enough context.
  • Do not overwrite memory files; append a short recovery note instead.
  • Do not persist secrets, tokens, or private message content that is not necessary for continuity.
  • Do not claim recovery is complete when the available history, logs, or memory sources are partial.

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?"

Privacy Guardrails (Required)

  • Default to minimum retrieval scope:

- last 24h or last 50 messages (whichever is smaller), unless user asks for more.

  • In DMs/private channels, require explicit user confirmation before broad history scans.
  • Do not persist recovered summaries to memory files without explicit approval.
  • Never include secrets/tokens in recovered summaries; replace with [REDACTED].

平台分布

Codex

33.39%
按下载量换算71

Claude

30.62%
按下载量换算66

Cursor

19.44%
按下载量换算42

Gemini CLI

8.03%
按下载量换算17

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

可疑

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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