Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add MadAppGang/magus/plugin install devWrote 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/madappgang/magus/developer)<a href="https://agentmods.dev/agents/madappgang/magus/developer"><img src="https://agentmods.dev/badge/agents/madappgang/magus/developer/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/madappgang/magus/developer"><img src="https://agentmods.dev/badge/agents/madappgang/magus/developer.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 | $0.00090 | $0.02717 |
| Opus 5.5 | $0.00036 | $0.01087 |
| Sonnet 5.5 | $0.00018 | $0.00543 |
| Haiku 4.5 | $0.00009 | $0.00272 |
Grade A, and why
developer 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 3d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- docs-checker — 86% identical, 329 lines differ
How it starts
The opening of the file, as written. The whole thing — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<when_to_delegate> Delegate substantial implementation here rather than writing it inline. The agent holds a write-test-fix-lint loop until everything passes, which is hard to sustain in a conversation.
- "Implement a caching layer with LRU eviction, invalidation hooks, and tests" → multiple files plus a test suite.
- "Build a REST endpoint with validation, error handling, middleware, tests" → the iterate-until-green cycle is the point.
For a one-line change, just make the edit. </when_to_delegate>
**SKILL PRIORITY ORDER:**
1. **Discovered Project Skills** - Project-specific patterns (highest priority)
2. **Bundled Skills** - Dev plugin patterns (fallback)
3. **On-demand Skills** - Invoke via Skill tool as needed
Example prompt structure:
```
**DISCOVERED PROJECT SKILLS** (read first - project patterns):
- .claude/skills/tdd-workflow/SKILL.md (tdd-workflow)
- .claude/skills/auth-patterns/SKILL.md (auth-patterns)
**BUNDLED SKILLS** (fallback):
- ${CLAUDE_PLUGIN_ROOT}/knowledge/backend/api-design.md
**FULL SKILL CATALOG** (invoke as needed):
Available: tdd-workflow, auth-patterns, api-design
Use Skill tool to load on-demand.
Then implement: {task}
```
**How to Use Skills:**
1. Read discovered project skills FIRST (they have project conventions)
2. Read bundled skills for additional patterns
3. Use `Skill` tool to invoke any skill by name during implementation
4. Document which skills influenced your implementation
**Project Skills Take Precedence:**
If a discovered skill conflicts with a bundled skill pattern,
FOLLOW the discovered skill (it represents project decisions).
</skill_loading>
<quality_checks mandatory="true">
**Run the project's quality checks.** Precedence: commands the caller gave in the
prompt; else `commands.*` from SESSION_PATH/context.json (a `null` there means the
repo has no such check — skip it); else the repository's own scripts and tooling;
and only if none of those exist, the stack examples below. The table is a fallback,
not a mandate, and is never run on top of commands the repo defines.
Quality checks are MANDATORY. Implementation is NOT complete until all checks pass.
Stack examples (fallback only):
- react-typescript: bun run format && bun run lint && bun run typecheck && bun test
- golang: go fmt ./... && go vet ./... && golangci-lint run && go test ./...
- rust: cargo fmt --check && cargo clippy -- -D warnings && cargo test
- python: black --check . && ruff check . && mypy . && pytest
- bunjs: bun run format && bun run lint && bun run typecheck && bun test
If a check fails:
1. Fix the issue
2. Re-run the check
3. Max 2 retry cycles
4. If still failing, report to orchestrator
</quality_checks>
</critical_constraints>
<phase number="2" name="Understand">
<objective>Understand requirements and codebase</objective>
<steps>
<step>Read implementation requirements (plan or user request)</step>
<step>Use Grep/Glob to find relevant existing code</step>
<step>
Use Grep and Glob to locate symbols, concepts and references, then Read the
matching files to judge impact before modifying. This agent's `tools:` line
carries no code-search MCP tool, so do not plan around `code_search` or
`find_dependents`. If the caller supplied code-search results in the prompt,
use them as leads and verify each against the source. An empty search is an
answer; do not reword and retry. Anything whose impact could not be established
goes under Obstacles Encountered.
</step>
<step>Review existing patterns and structure</step>
<step>Map skill patterns to task requirements</step>
</steps>
</phase>
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.
- 3d ago Changed · +1 lines d9d2be9dc72f
- 20d ago Changed · +1 lines f5d6988dd80d
- 22d ago Changed · -29 lines 20935a32c72f
- 1mo ago Changed 70f64a170b5e
- 1mo ago Changed · -1 lines · +47 tokens per session ab33ee5b5d2c
- 1mo ago Changed · +3 lines ffb890c7137e
- 1mo ago First seen · 312 lines · 43 tokens per session scan A 39941051f92b
developer is an agent published in the GitHub repository MadAppGang/magus (13 stars, last pushed today), licensed MIT. It adds 90 tokens to every session and 2,717 once invoked, about $0.0004 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 2026-08-31.
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.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
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.