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.
npx skills add legendtkl/agentic-skill-router --skill skill-073git clone --depth 1 https://github.com/legendtkl/agentic-skill-routerWrote 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/skills/legendtkl/agentic-skill-router/skill-073)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-073"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-073/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/skills/legendtkl/agentic-skill-router/skill-073"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-073.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 | $0.00025 | $0.01272 |
| Opus 5 | $0.00013 | $0.00636 |
| Sonnet 5 | $0.00005 | $0.00254 |
| Haiku 4.5 | $0.00003 | $0.00127 |
Grade A, and why
skill-073 scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Worker Protocol
"Complete work, send message via pipe, exit. Supervisor restarts if needed."
When to Use This Skill
Use when:
- Implementing worker agent behavior
- Understanding when/how to exit
- Understanding supervisor auto-restart
Use proactively:
- Reference before implementing agent message loops
- After completing work, before exiting
Quick Start
Connect to watchdog
Connect-ToWatchdog -AgentName "developer" -SessionDir "..claude\session"
Enter message loop
Enter-AgentLoop -MessageHandler { param($Message) switch ($Message.type) { "WorkAssign" { Send-WorkComplete -TaskId $Message.payload.taskId -Result "success" } } }
Example 2: Send work complete
```powershell
Send-WorkComplete -TaskId "feat-001" -Result "success" -Notes "Complete"
Example 3: Exit conditions
- Work complete →
WorkComplete→ Exit (code 0) - Need clarification →
Query→ Exit (code 0) - Shutdown received → Exit (code 42)
</examples>
---
## Architecture
┌─────────────────────────────────────────────────────────────┐ │ ACTOR SUPERVISOR (Watchdog) │ │ 1. StartActor(agentName) - creates bidirectional pipe │ │ 2. Wait for agent connection via agent-runtime.ps1 │ │ 3. Supervise() - check for crashes, restart with backoff │ │ 4. Route messages between agents via pipes │ │ 5. StopAll() - graceful shutdown │ └───────────────────────────┬───────────────────────────────────┘ │ ┌───────────▼─────────────┐ │ PM ↔ Workers (pipes) │ │ Event Log (source) │ └──────────────────────────┘
---
## Agent Lifecycle
┌─────────────┐ ┌──────────────┐ ┌─────────────┐ │ CONNECT │ ──▶ │ DO WORK │ ──▶ │ EXIT │ │ (agent- │ │ (receive via │ │ (supervisor │ │ runtime) │ │ pipe, │ │ may restart)│ └─────────────┘ │ process) │ └─────────────┘ └──────────────┘
**Key Principle**: Each agent run processes messages, completes work, exits. Supervisor restarts when needed.
---
## Event-Driven vs Sequential Mode
| Aspect | Event-Driven | Sequential |
|--------|-------------|------------|
| Agent spawning | On demand | One at a time |
| Message delivery | Named pipes | Handoff file |
| Parallel work | Yes | No |
| Startup | `/ralph-coordinator-event` | `/ralph-coordinator-single` |
| Connection | `agent-runtime.ps1` | Handoff files |
---
## Exit Conditions
| Condition | Action | Exit Code | Restarted |
|-----------|--------|-----------|-----------|
| Work complete | Send `WorkComplete` | 0 | No (new task will spawn) |
| Need clarification | Send `Query` | 0 | No |
| Blocking issue | Send `WorkBlocked` | 0 | No |
| Shutdown received | - | 42 | No |
| Crash | - | Non-zero | Yes (with backoff) |
**Supervisor restart strategy**:
- Exponential backoff: 5s, 10s, 20s, 40s, 60s (max)
- Max 3 restarts per agent
- Graceful exits (0 or 42) are not restarted
---
## V2 vs V1
| Aspect | V1 (Legacy) | V2 (Current) |
|--------|-------------|--------------|
| Agent lifecycle | File queue monitoring | Pipe-based event loop |
| Delivery latency | 2-5 seconds | <10 milliseconds |
| Crash recovery | Manual health checks | Automatic (ActorSupervisor) |
| State persistence | Multiple files | Single event log |
| Message types | 47+ types | 12 core types |
| Restart strategy | None | Exponential backoff |
---
## Complete Worker Example
```powershell
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.
- 6d ago First seen · 178 lines · 25 tokens per session scan A 0fb8101f161c
skill-073 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 25 tokens to every session and 1,272 once invoked, about $0.0001 per session on Opus 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 2026-09-03.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…