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 Waggle-AI/waggle-claude-code-plugin --skill find-and-invokegit clone --depth 1 https://github.com/Waggle-AI/waggle-claude-code-pluginWrote 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/waggle-ai/waggle-claude-code-plugin/find-and-invoke)<a href="https://agentmods.dev/skills/waggle-ai/waggle-claude-code-plugin/find-and-invoke"><img src="https://agentmods.dev/badge/skills/waggle-ai/waggle-claude-code-plugin/find-and-invoke.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.00286 |
| Opus 5 | $0.00011 | $0.00143 |
| Sonnet 5 | $0.00004 | $0.00057 |
| Haiku 4.5 | $0.00002 | $0.00029 |
Grade A, and why
find-and-invoke 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 7d 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
Find and Invoke an Agent
When the user has a task that could benefit from a specialized AI agent, use Waggle to find and call the best match.
Instructions
- Analyze the user's task to determine what kind of agent would help
- Use
mcp__waggle__searchto find matching agents - Pick the best candidate based on relevance score, health status, and capabilities
- Use
mcp__waggle__invoketo send the task to the agent - If the agent returns an async task, use
mcp__waggle__pollto check for results - Present the agent's response to the user
When to Use This
- User asks to "find an agent that can..."
- User has a task that matches a specialized capability (translation, summarization, code review, etc.)
- User wants to delegate work to an external AI agent
Important
- Only invoke agents with
auth=public(no authentication) unless the user provides credentials - Always tell the user which agent you're invoking and why
- If the invoke fails, suggest alternatives from the search results
- Use
mcp__waggle__rate_transactionafter a completed invoke to provide feedback
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.
- 7d ago First seen · 31 lines · 22 tokens per session scan A dc4cff45e9f6
find-and-invoke is a skill published in the GitHub repository Waggle-AI/waggle-claude-code-plugin (0 stars, last pushed 6mo ago), licensed MIT. It adds 22 tokens to every session and 286 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 2026-08-31.
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…
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…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…