Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/JannLeo/telinksdk-builder-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/jannleo/telinksdk-builder-mcp/watchdog)<a href="https://agentmods.dev/commands/jannleo/telinksdk-builder-mcp/watchdog"><img src="https://agentmods.dev/badge/commands/jannleo/telinksdk-builder-mcp/watchdog/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/jannleo/telinksdk-builder-mcp/watchdog"><img src="https://agentmods.dev/badge/commands/jannleo/telinksdk-builder-mcp/watchdog.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.00403 |
| Opus 5.5 | $0.00016 | $0.00161 |
| Sonnet 5.5 | $0.00008 | $0.00081 |
| Haiku 4.5 | $0.00004 | $0.00040 |
Grade A, and why
watchdog scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
管理 Trae CLI 后台会话(数据来自本机 trae-cli 的 sessions 目录)。调用 ${COCO_PLUGIN_ROOT}/scripts/watchdog_cli.py,不要自己拼命令或直接读 events.jsonl。
根据用户参数 $ARGUMENTS 选择子命令,构造命令:python ${COCO_PLUGIN_ROOT}/scripts/watchdog_cli.py <子命令> [参数],用 Bash 工具执行后解析 stdout 的 JSON 向用户汇报。
- 无参数 或
status:列出最近 24h 活跃会话及其状态。汇报每个会话的 short_id、标题、状态(working 工作中 / idle_waiting 空闲等输入 / error 报错 / unknown),error状态要特别标出并建议/watchdog restart <id>。 restart <会话ID>:压缩上下文并发送"继续之前的任务"指令(会话 ID 可用完整 ID 或前 8 位)。汇报 compact_ok / resume_ok。continue <会话ID> [提示词]:只发送继续指令(默认"继续之前的任务",可自定义提示词)。汇报 ok 与 session_id。
注意:
- 会话 ID 为空时脚本默认取最活跃的会话。
- 若输出含 "未找到 trae-cli",提示用户设置 TRAE_CLI 环境变量或在 PATH 中安装。
- 所有操作只影响 trae-cli 后台会话,与本插件构建功能无关。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 18 lines · 0 tokens per session scan A a151dae145e2
watchdog is a command published in the GitHub repository JannLeo/telinksdk-builder-mcp (1 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 403 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-15.
Other commands, from other repositories
ws
Word summary — anything told at N levels, from N words down to 1 word, for anything: a topic ("russian literature"), a URL, YouTube link, file, pasted text, a question, or "this". A leading number sets N (5 by default). Shortcut for the Fabric skill's WordSummary intent.
cancel-ralph
Cancel active Ralph Loop.
engage.actions
Execute Phase 7 - Actions on Objectives and Goal Achievement.
r
Resume the last handoff and carry on with the outstanding work.
quickstart
Interactive onboarding guide — set up credentials, connect to orq.ai, and learn every command and skill.
focus
You are an ADHD focus assistant. Help the user commit to ONE task and block mental distractions.