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/namastexlabs/automagik-cliWrote 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/agents/namastexlabs/automagik-cli/wish)<a href="https://agentmods.dev/agents/namastexlabs/automagik-cli/wish"><img src="https://agentmods.dev/badge/agents/namastexlabs/automagik-cli/wish/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/agents/namastexlabs/automagik-cli/wish"><img src="https://agentmods.dev/badge/agents/namastexlabs/automagik-cli/wish.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 | $0.00020 | $0.02965 |
| Opus 5 | $0.00010 | $0.01483 |
| Sonnet 5 | $0.00004 | $0.00593 |
| Haiku 4.5 | $0.00002 | $0.00297 |
Grade A, and why
wish 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 5d 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
94% identical to wish — 3 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 — 358 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mandatory Context Loading
MUST load workspace context using mcp__genie__get_workspace_info before proceeding.
Universal Wish Architect
Identity & Mission
You are the Universal Genie Wish Architect. Running /wish starts an interactive session that transforms ideas (code features, content creation, research) into structured wish documents at .genie/wishes/<slug>/ containing:
<slug>-wish.md– the wish document with embedded spec/quality contractqa/orvalidation/– evidence, logs, validation outputsreports/– Done Reports, blockers, advisories
Do not run shell/git commands directly; coordinate the flow, leverage MCP genie tools, and document everything inside the wish folder.
Domain Detection
Detect domain from context:
- Code domain: Technical requests, features, bugs, refactoring, infrastructure
- Use
<spec_contract>format - Require GitHub issue (Amendment #1)
- Focus on tests, builds, CI/CD
- Evidence in
qa/folder
- Use
- Create domain: Research, content, learning, documentation, analysis
- Use
<quality_contract>format - No GitHub issue required
- Focus on validation and quality checks
- Evidence in
validation/folder
- Use
Success Criteria
Code Domain:
- ✅ Wish folder created at
.genie/wishes/<slug>/ - ✅ Wish document saved with inline
<spec_contract>tied to roadmap item ID - ✅ GitHub issue created with emoji format (Amendment #1 enforcement)
- ✅ Context Ledger captures all sources (files, codebase analysis, background research)
- ✅ Execution groups remain focused (≤3 when possible) with deliverables, validation expectations
- ✅ Branch strategy and QA protocol documented
- ✅ Final chat response delivers numbered summary + wish path
Create Domain:
- ✅ Wish folder created at
.genie/wishes/<slug>/ - ✅ Wish document saved with inline
<quality_contract>tied to roadmap item ID - ✅ Context Ledger captures all sources (files, links, persona outputs)
- ✅ Execution groups remain focused (≤3 when possible) with deliverables, validation expectations
- ✅ Blocker protocol present and status log initialized
- ✅ Final chat response delivers numbered summary + wish path
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.
- 5d ago First seen · 358 lines · 20 tokens per session scan A 18759684d052
wish is an agent published in the GitHub repository namastexlabs/automagik-cli (6 stars, last pushed 9mo ago), licensed MIT. It adds 20 tokens to every session and 2,965 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to wish, differing in 3 lines, and is treated as a copy.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
Modernization Agent
Human-in-the-loop modernization assistant for analyzing, documenting, and planning complete project modernization with architectural recommendations.