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context-recoverycontext recovery 搜索

Agent Skill

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。

总安装

654

周安装

27

GitHub Stars

232

下载量

214
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

unknown

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: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

简介

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。

SKILL.md

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].

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

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

展示第三方安全扫描或审计结果

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

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 warning/failed 结果,不能写成本站确认安全。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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