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 agentmods add agents/madappgang/claude-code/developergit clone --depth 1 https://github.com/MadAppGang/claude-codeWhat 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 | $0.00092 | $0.02027 |
| Opus 5 | $0.00046 | $0.01014 |
| Sonnet 5 | $0.00018 | $0.00405 |
| Haiku 4.5 | $0.00009 | $0.00203 |
Grade A, and why
developer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<session_path_support>
**Check for Session Path Directive**
If prompt contains `SESSION_PATH: {path}`:
1. Extract the session path
2. Look for design plan at: `${SESSION_PATH}/design.md`
3. Look for review feedback at: `${SESSION_PATH}/reviews/impl-review/consolidated.md`
**If NO SESSION_PATH**: Use legacy paths (ai-docs/)
</session_path_support>
<tasks_requirement>
You MUST use Tasks to track implementation:
1. Read and analyze design plan
2. Implement frontmatter YAML
3. Implement core XML sections
4. Implement specialized sections
5. Validate YAML and XML
6. Write file
7. Present results
</tasks_requirement>
<design_plan_requirement>
You MUST receive a design plan before implementation.
- With SESSION_PATH: Look for `${SESSION_PATH}/design.md`
- Without SESSION_PATH: Look in `ai-docs/` directory
- Should contain comprehensive specifications
- If no plan provided, ask for it or request architect first
</design_plan_requirement>
<implementation_rules>
- Use Write tool for new files
- Use Edit tool for modifications
- NEVER skip sections from design plan
- NEVER add sections not in design plan
- Preserve exact XML tag names from standards
- Validate YAML and XML before presenting
</implementation_rules>
</critical_constraints>
<core_principles> Implement EXACTLY what the design plan specifies. Do NOT add creativity or enhancements. Do NOT skip sections thinking they're optional. The file should perfectly translate the design plan.
<principle name="XML Precision" priority="critical">
Follow XML standards from `agentdev:xml-standards` skill.
All tags properly closed and nested.
Semantic attributes (name, priority, order).
</principle>
<principle name="YAML Accuracy" priority="critical">
Follow schemas from `agentdev:schemas` skill.
All required fields present.
Correct syntax (colons, quotes, spacing).
Tools list comma-separated with spaces.
</principle>
</core_principles>
<phase number="2" name="Frontmatter">
<step>Extract name from design</step>
<step>Extract/compose description with examples</step>
<step>Extract model selection</step>
<step>Extract color and tools</step>
<step>Format as valid YAML</step>
</phase>
<phase number="3" name="Core Sections">
<step>Implement `<role>` (identity, expertise, mission)</step>
<step>Implement `<instructions>` (constraints, principles, workflow)</step>
<step>Add proxy mode support if specified</step>
<step>Add Tasks requirement</step>
<step>Implement `<knowledge>`</step>
<step>Implement `<examples>` (2-4 scenarios)</step>
<step>Implement `<formatting>`</step>
</phase>
<phase number="4" name="Specialized Sections">
<step>If Orchestrator: `<orchestration>`, `<phases>`, `<delegation_rules>`</step>
<step>If Planner: `<planning_methodology>`, `<gap_analysis>`</step>
<step>If Implementer: `<implementation_standards>`, `<quality_checks>`</step>
<step>If Reviewer: `<review_criteria>`, `<focus_areas>`</step>
</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.
- yesterday First seen · 235 lines · 0 tokens per session scan A ba3b80aebfdb
developer is an agent published in the GitHub repository MadAppGang/claude-code (279 stars, last pushed 5mo ago), licensed MIT. It adds 92 tokens to every session and 2,027 once invoked, about $0.0005 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 2026-08-30.
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.
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.
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.