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Agent Skill · 研究检索

session-janitor(会话管理员)

session-janitor 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

session-janitor 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,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 16,476。

总安装 7,173周安装 293GitHub Stars 公开资料未说明下载 2,297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
MIT-0
版本
1.5.0

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-janitor(会话管理员)
来源仓库:https://github.com/halfdeadcat/session-janitor
安装命令:
openclaw skills install session-janitor
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install session-janitor

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。

会话管理员用于自动修剪转录文本并提取 LLM 结构化记忆。适合防止抄本膨胀,提升长期会话的信息密度。可集成到 OpenClaw 网关,实现端到端的会话健康管理。使用前请确认日志读取权限,避免因访问受限导致功能失效。session-janitor 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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 OpenClaw;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 openclaw skills install session-janitor;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

    它需要接触敏感数据,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-janitor 怎么安装?

在终端执行 openclaw skills install session-janitor。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

session-janitor 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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-janitor 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

session-janitor 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

session-janitor 用的人多吗?

公开数据是累计安装 7,173 次,近一周安装 293 次,下载 2,297 次。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-janitor 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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 OpenClaw,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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
session-janitor

会话管理员 · 本页

7,173—接触敏感数据MIT-0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

name
janitor
description
Automated transcript trimming, LLM memory extraction, and session hygiene for OpenClaw gateways. Keeps transcripts from bloating, extracts structured memories before archiving, and prunes stale sessions.
metadata
{"openclaw": {"requires": {"bins": ["python3", "jq"]}}}

Session Janitor

Automated transcript and session hygiene for OpenClaw gateways.

What It Does

  • Sidecar offloader — extracts large tool outputs (≥5KB) to .toolcache/ files, replacing inline content with lightweight stubs. Runs before trim on every turn.
  • Trims oversized transcripts — keeps recent exchanges + synthetic compaction summary, archives the rest
  • LLM memory extraction — uses the gateway's chat completions API to extract structured memories (facts, decisions, lessons) from trimmed content before discarding
  • Prunes stale sessions — removes old subagent/cron session entries from sessions.json
  • Archives orphan transcripts — files with no active session after a grace period
  • Cleans old archives — removes pre-trim, reset, and checkpoint files after retention period
  • Cleans orphaned checkpoints — removes *.checkpoint.*.jsonl files left behind by auto-compaction once the parent session no longer has an active transcript

Setup

bash skills/session-janitor/scripts/setup.sh

Setup auto-discovers all gateway installations (~/.openclaw/, ~/.openclaw-*/), generates config.json, installs a cron job, and installs the watcher service (systemd on Linux, launchd on macOS).

macOS Prerequisites

Install fswatch for the per-turn watcher:

brew install fswatch

The cron-based janitor works without it (trimming every 15 min instead of within ~3s of each turn).

For architecture details, trimming mechanics, and token math, see ARCHITECTURE.md.

Configuration

Edit config.json after setup to tune thresholds:

KeyDefaultDescription
trimMaxKB250Trim transcripts larger than this. Trim always fires when exceeded — there is no minimum pair count.
keepPairs10Number of recent user/assistant pairs to keep after trim. If fewer pairs exist than this, all are kept (no skip).
keepFullPairs2Most recent N assistant turns that keep full toolResult bodies (older turns are collapsed to summary lines)
minArchivePairs5Informational only — no longer blocks trim. Sessions exceeding trimMaxKB are always trimmed.
trimFullThresholdPct50Two-stage aggressive reduction when trimmed output still exceeds this % of trimMaxKB: (1) strip all assistant turns (tool args + thinking removed); (2) if still over threshold, drop all toolResult entries entirely. Set to 100 to disable.
archiveRetentionDays7Delete old archives after this many days (applies to .reset.*, .deleted.*, .pre-trim.*, .bak-*, .purged.*, .emergency-*)
keepPreTrimFiles3Max .pre-trim.* files to keep per session (oldest deleted immediately, regardless of retention period)
orphanGraceMinutes30Wait before archiving orphan transcripts
staleSubagentHours24Prune subagent session entries older than this
staleCronSessionHours24Prune cron session entries older than this
llmExtraction.enabledtrueUse LLM to extract memories from trimmed content
llmExtraction.modelopenclawModel identifier to use for extraction calls
llmExtraction.maxInputChars20000Max characters of archived content sent to LLM
llmExtraction.timeoutSecs60LLM API call timeout in seconds
llmExtraction.maxMemories15Max memories to accept from a single LLM extraction
llmExtraction.minArchived3Minimum archived messages required before running LLM extraction
llmExtraction.maxPerRun1Max LLM extractions per cron cycle (cost control)
memCli.enabledfalseStore extracted memories via mem CLI (requires mem)
cronSchedule*/15 * * * *How often to run
sidecar.enabledtrueOffload large toolResult entries to .toolcache/ files
sidecar.minEntryBytes5120Minimum toolResult content size (bytes) to trigger offload
watchdog.enabledfalseRun hung-session detector after each janitor pass
watchdog.staleMinutes5Session updatedAt age threshold to consider stuck
watchdog.alertSlacktrueSend Slack DM when a stuck session is detected
watchdog.slackTarget—Slack user ID or channel to notify
watchdog.autoRestartfalseAuto-trigger safe-restart script on detection (use with caution)
watchdog.restartScriptSlack~/bin/safe-slack-restart.shSafe restart script path for Slack gateway
watchdog.restartScriptDiscord~/bin/safe-gateway-restart.shSafe restart script path for Discord gateway

Gateway Discovery

Setup scans for ~/.openclaw/openclaw.json and ~/.openclaw-*/openclaw.json. Each discovered gateway gets its own entry in config.json with:

  • Port (from gateway.port in config)
  • Auth token (from gateway.auth.token)
  • Sessions directory path

Works with single-gateway installs, multi-gateway (Discord + Slack), or any custom layout.

LLM Extraction Guardrails

  • Lockfile — only one extraction at a time across all gateways
  • Max per run — configurable cap per cron cycle (default: 1)
  • 20K char input cap — truncates middle of very long conversations
  • 60s hard timeout — SIGALRM kills hung API calls
  • Dedup — state file tracks processed trims, never re-runs
  • No context inflation — memories go to mem DB only, never to files that get auto-loaded into session context

Manual Run

bash skills/session-janitor/scripts/janitor.sh

Logs

tail -f /tmp/session-janitor.log

Watcher Service

The watcher fires trim within ~3 seconds of each turn completing (vs. waiting for the next cron cycle). setup.sh installs it automatically as a system service.

*Linux (systemd):*

systemctl --user status session-janitor-watcher
systemctl --user restart session-janitor-watcher
tail -f /tmp/session-janitor-watcher.log

*macOS (launchd):*

launchctl list | grep session-janitor
launchctl unload ~/Library/LaunchAgents/ai.openclaw.session-janitor-watcher.plist
launchctl load  ~/Library/LaunchAgents/ai.openclaw.session-janitor-watcher.plist
tail -f /tmp/session-janitor-watcher.log
  • *Linux* uses inotifywait (from inotify-tools) and watches via inotify kernel events.
  • *macOS* uses fswatch (via Homebrew) and watches via FSEvents.

Watchdog

The watchdog runs after every janitor pass and checks updatedAt on all active sessions. If any session is stale longer than watchdog.staleMinutes, it fires a Slack alert. Optional autoRestart will trigger the appropriate safe-restart script.

Alert cooldown is 10 minutes per session to prevent spam. State is tracked in the janitor state file.

# Run standalone
bash skills/session-janitor/scripts/watchdog.sh 5 ~/.openclaw/session-janitor-state.json

Sub-Agent Reporting Conventions

Workers spawned via Foreman should use prefixed sessions_send messages to prevent the parent agent from spawning new sub-agents in response to status updates:

# Intermediate status (fire-and-forget — parent relays but does NOT spawn)
sessions_send(sessionKey="{PARENT}", message="STATUS: 🤖 [label]: doing X (60%)", timeoutSeconds=0)

# Errors/failures (normal delivery — parent may intervene)
sessions_send(sessionKey="{PARENT}", message="ERROR: 🤖 [label]: what failed — details")

# Completion (no prefix, normal delivery)
sessions_send(sessionKey="{PARENT}", message="🤖 [label]: ✅ done — summary")
  • STATUS: → relay-only; timeoutSeconds=0 prevents reply-back loops
  • ERROR: → parent receives and considers recovery action
  • No prefix → completion; parent handles and relays to channel

Files

skills/session-janitor/
├── SKILL.md                              # This file
├── ARCHITECTURE.md                       # Deep dive: trimming mechanics, token math, examples
├── config.json                           # Generated by setup (gitignored)
├── config.example.json                   # Reference config
├── session-janitor-watcher.service       # Linux: systemd user service template
├── session-janitor-watcher.plist         # macOS: launchd LaunchAgent template
└── scripts/
    ├── setup.sh           # Discovery + config gen + cron + service install (Linux + macOS)
    ├── janitor.sh         # Main cron entry point
    ├── trim.py            # Transcript trimming (thinking, toolCall args, toolResult collapse)
    ├── sidecar.py         # Large toolResult offloader (→ .toolcache/ files)
    ├── watcher.sh         # Per-turn trigger (sidecar + trim; inotifywait/fswatch)
    ├── extract-llm.py     # LLM memory extraction
    ├── prune-sessions.py  # Stale session pruning
    ├── watchdog.sh        # Hung-session detector + Slack alert
    ├── test-sidecar.py    # Sidecar test suite (8 scenarios, 22 assertions)
    └── test-sidecar.sh    # Shell wrapper for sidecar tests

平台分布

OpenClaw

75.28%
按下载量换算1,729

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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