Songhonglei

Manage OpenClaw Agent's built-in memory features — enable/disable/configure Dreaming (Light→REM→Deep auto memory consolidation) and Active Memory (proactive memory injection with three style presets: conservative / balanced / aggressive). Auto-detects K8s ConfigMap mirrors, creates *.bak.<timestamp> backups before write, supports --dry-run preview, --style preset for active-memory, --half-life / --max-age / --timezone for dreaming, and triggers gateway restart automatically (opt-out via --no-restart). Triggers: "enable dreaming", "tune memory half-life", "configure agent memory", "enable active memory", "switch memory style", "conservative/balanced/aggressive memory mode".

管理 OpenClaw 容器启动时自动执行的脚本钩子。gateway 重启、pod 重启、pod 重建均不丢失（数据持久化在 workspace/.init-hooks/）。 支持 inline 命令、本地脚本（shell/python）、CDN URL（zip/tar.gz 自动下载解压）三种钩子类型。 用于：新增/查看/修改/禁用/删除启动钩子、立即测试运行、查看执行日志。 触发词：init-hooks、启动钩子、startup hook、重启初始化、pod重启执行、开机自启。 安装触发词：帮我安装 init-hooks、初始化 init-hooks、设置 init-hooks、执行 init-hooks 安装。 配置变更触发词：修改 openclaw.json、改 gateway 配置、改配置文件 → 执行后自动调用 sync_openclaw_config.py。

Team-wide P2P mesh for OpenClaw agents running on different containers/pods. Each agent's gateway listens on its own pod IP:18789 over WebSocket; the mesh CLI lets you ping, send-and-await-reply, broadcast, and discover the whole team. No broker, no Supabase, no central server — just direct WS calls between teammates' agents. Includes auto-detect of "this machine's identity" (USER.md / sso.json / env var), secure token storage (separate chmod 600 file, not committed to git), message size limits (4KB warn / 8KB block), --dry-run preview for both send and broadcast, and an optional IM fallback hook when an agent is unreachable. Triggers: "message my teammate's agent", "ping bob's agent", "broadcast to the team", "agent mesh", "team agent communication".

One-click subagent timeout configurator for OpenClaw. Configures the three related fields (agents.defaults.timeoutSeconds + subagents.runTimeoutSeconds + acp.runtime.ttlMinutes) atomically with built-in TTL constraint validation (ttlMinutes * 60 ≥ runTimeoutSeconds). Three preset profiles — quick (60s/1h/1h, fast feedback), normal (3min/2h/2h, everyday dev), patient (5min/3h/3h, long-running tasks) — plus --custom for arbitrary values. Auto-backup before write, optional gateway auto-restart, --dry-run preview. Triggers: "set subagent timeout", "subagent 超时", "配置 timeout", "调整超时", "subagent timeout config", "openclaw timeout", "tool call timeout".

Extract a standardized cross-tool user profile from any AI agent's long-term memory (USER.md, MEMORY.md, AGENTS.md, CLAUDE.md, .cursorrules, etc.) so you can re-use it in another AI tool without retraining. Output is a portable Markdown document with five categories — instructions, identity, profession, projects, preferences. Works with Claude Code, OpenClaw, Codex CLI, Cursor, Cline, Continue, Aider, or any LLM agent. 适用场景:生成我的画像/导出 profile/ 拷贝到其他 AI 工具/跨工具同步偏好/换 agent 不丢记忆。

Manage your OpenClaw Agent's avatar. Send an image/URL directly, or describe a style and let Freepik vector search pick candidates for you. Auto-saves to the workspace avatars/ directory and updates IDENTITY.md. 当用户说「换头像」「找一张头像」「更新头像」「给我换个头像」「change avatar」时使用。 Requires a free Freepik API Key for auto-search mode (direct image/URL mode works without it). OpenClaw-only — relies on `openclaw agents set-identity`, `IDENTITY.md`, and `TOOLS.md` conventions.

Broadcast a single memory note to multiple AI-agent workspaces in one shot, upserting into MEMORY.md / AGENTS.md / TOOLS.md / USER.md across all targets. Pure file-ops, zero network, zero LLM. Works on OpenClaw, Claude Code, Cursor, and any agent runtime that stores per-project memory as Markdown. Triggers: "broadcast memory", "tell every agent", "remember this everywhere", "sync this to all agents", "make all my agents know", "集体记忆", "广播这条记忆", "你们全都记住", "所有agent记住", "大家都记一下", "让他们都记住".

Rename or auto-generate a friendly label for an OpenClaw-style session by editing sessions.json directly. Supports random labels (zh/en with locale auto-detect), multi-agent auto-detection, listing sessions, retry with verification, and XDG-compliant history storage. 适用场景:在 OpenClaw / 任何兼容的 session 管理体系中给会话起名/改名/换花名/重命名 session/给会话加 label。

Quickly open-source a local skill to GitHub (primary) and optionally clawhub.com. Workflow: slug pre-check, fork to opensourceskills, strip internal info, normalize SKILL.md, generate LICENSE/README, init git, push to GitHub with configurable token source, and optionally publish to clawhub. Use when the user says "open-source this skill", "把这个 skill 开源", "发到 github", "publish skill publicly", or "把本地 skill 发开源". Hard rules: never modify the original skill in place, never auto-add force/yes flags, never write tokens to git/remote/memory, and ask the user at decision points.

Query, install, update, and edit AI agent skills on any compatible Skill Hub (self-hosted, or any Hub implementing the documented API contract). Dual-channel: with a token it uses the authenticated API (full features including private skills); without a token it falls back to the unauthenticated channel (public skills only). Covers: list newly published skills, search by keyword / author / time / source, inspect version history, install or upgrade a specific version, and edit a skill's card metadata (display name, summary, tags, visibility, applicable position, etc.) via a safety-first GET -> diff -> backup -> PUT -> dual-channel verify -> auto-rollback flow. Trigger phrases include "what's new on the hub", "search for X skill", "install X", "update Y skill", "edit hub card info", "show skill version history", "skill-hub-query".

Install and operate the OpenClaw skill-sediment plugin extension (turns successful conversations into auto-generated SKILL.md files, then promotes them to skills/). Covers: install (with interactive agent selection), heal (self-repair: detect and fix whitelist/file loss, supplement missing validAgentId, auto-restart gateway), doctor (diagnostics), recover (restore after pod restart), status (sediment pool stats), config (current state), uninstall. Triggers on: sediment / auto-skill-sediment / install/port/clone sediment / sediment not working / sediment pool / uninstall sediment. Note: unrelated to the same-named Rust semantic-memory MCP (store/recall); does not cover OpenClaw installation itself nor publishing to Skill Hubs.

End-to-end guided workflow for building production-quality agent skills. Walks users through 4 stages: pre-flight checks → spec confirmation (3 musts) → coding quality acceptance (4 checks) → release prep (5 requirements). Use when the user says "create a skill", "build a skill", "write an agent skill", "skill-build-wizard", "做个 skill", "帮我创建一个 skill", "skill 全流程向导". Calls skill-creator for actual scaffolding and routes to glic-check / skill-release-audit / skill-release-plus / skill-regression for verification and release. Not for: just writing one quick skill file (use skill-creator directly); just publishing an already-finished skill (use skill-release-plus).

Regression testing framework for AgentSkills. Analyzes a target skill, runs script-layer assertions and AI-layer semantic scoring, and outputs a Markdown report. Supports two backends: OpenAI-compatible LLM (default, works anywhere) or OpenClaw cron-based agent trigger (auto-detected if openclaw CLI present). Use when the user asks to "test a skill", "regression test xxx skill", "run skill QA", or "audit my skill against test cases". Requires SR_LLM_API_KEY env var or interactive setup; supports .env files.

Pre-publish quality and safety auditor for AI agent skills (SKILL.md + scripts/ + references/ format used by Claude Code, Cursor, OpenAI Codex, GitHub Copilot, OpenClaw, ClawHub, and compatible SkillHub registries). Six static-check modules (no LLM, no network by default): (1) syntax and logic correctness, (2) feature completeness, (3) edge-case and error handling, (4) data safety (detects files written inside the skill dir that would be lost on update), (5) dependency declaration vs code, (6) SKILL.md documentation standards. Per-registry rule profiles via `--target`. Pure reporter — never edits your files, never publishes. Use when publishing a skill, modifying an existing skill, or diagnosing why a skill behaves unexpectedly — run it as the last machine-checkable gate before release. Trigger phrases: "skill release audit", "audit before publishing", "pre-release check", "release gate", "skill safety check", "发版前检查", "skill 发布检查", "审查这个 skill 能不能发版".

Multi-hub skill publisher. Sign → Audit (placeholder) → Pack (clean tar.gz with exclude rules) → Publish to clawhub.com, skillhub.cn (Tencent Cloud), or GitHub Releases, all in one command. Pluggable adapter framework — add your own hub via a user-hook script (subprocess + JSON envelope). Single source of truth: one exclude.json, one signing pass, one package, fan out to N targets. Default target: clawhub only. Use --target all for all three real hubs, or --target user-hook:./my-script.sh for custom destinations. Triggers on: publish skill, release skill, push to skill hub, ship skill to multiple hubs, 发布 skill, 上传到 clawhub, 发布到 skillhub.cn.

Cryptographically sign and verify AgentSkill directories using Ed25519 (RFC 8032). Publishers sign skills with a private key; recipients verify with a public key, proving the skill is unmodified AND authored by the trusted key holder. Pure Python — no pip install, no native dependencies. Use when you say sign skill, verify skill, ed25519 sign, verify skill authenticity, prove skill author, detect skill tampering, give skill a signature, 给 skill 签名, 验证 skill 是谁发的, 校验 skill 没被改过.