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 proffesor-for-testing/agentic-qe --skill worker-integrationgit clone --depth 1 https://github.com/proffesor-for-testing/agentic-qeWrote 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/proffesor-for-testing/agentic-qe/worker-integration)<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/worker-integration"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/worker-integration/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/proffesor-for-testing/agentic-qe/worker-integration"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/worker-integration.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 | $0.00014 | $0.00895 |
| Opus 5.5 | $0.00006 | $0.00358 |
| Sonnet 5.5 | $0.00003 | $0.00179 |
| Haiku 4.5 | $0.00001 | $0.00089 |
Grade A, and why
worker-integration 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.
This is a copy
100% identical to worker-integration — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Worker-Agent Integration Skill
Intelligent coordination between background workers and specialized agents.
Quick Start
# View agent recommendations for a trigger
npx agentic-flow workers agents ultralearn
npx agentic-flow workers agents optimize
# View performance metrics
npx agentic-flow workers metrics
# View integration stats
npx agentic-flow workers stats --integration
Agent Mappings
Workers automatically dispatch to optimal agents based on trigger type:
| Trigger | Primary Agents | Fallback | Pipeline Phases |
|---|---|---|---|
ultralearn |
researcher, coder | planner | discovery → patterns → vectorization → summary |
optimize |
performance-analyzer, coder | researcher | static-analysis → performance → patterns |
audit |
security-analyst, tester | reviewer | security → secrets → vulnerability-scan |
benchmark |
performance-analyzer | coder, tester | performance → metrics → report |
testgaps |
tester | coder | discovery → coverage → gaps |
document |
documenter, researcher | coder | api-discovery → patterns → indexing |
deepdive |
researcher, security-analyst | coder | call-graph → deps → trace |
refactor |
coder, reviewer | researcher | complexity → smells → patterns |
Performance-Based Selection
The system learns from execution history to improve agent selection:
// Agent selection considers:
// 1. Quality score (0-1)
// 2. Success rate
// 3. Average latency
// 4. Execution count
const { agent, confidence, reasoning } = selectBestAgent('optimize');
// agent: "performance-analyzer"
// confidence: 0.87
// reasoning: "Selected based on 45 executions with 94.2% success"
Memory Key Patterns
Workers store results using consistent patterns:
{trigger}/{topic}/{phase}
Examples:
- ultralearn$auth-module$analysis
- optimize$database$performance
- audit$payment$vulnerabilities
- benchmark$api$metrics
Benchmark Thresholds
Agents are monitored against performance thresholds:
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 · 155 lines · 14 tokens per session scan A 7c82f899ca48
worker-integration is a skill published in the GitHub repository proffesor-for-testing/agentic-qe (512 stars, last pushed yesterday), licensed MIT. It adds 14 tokens to every session and 895 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to worker-integration, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
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…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
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…