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/jellydn/my-ai-toolsWrote 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/jellydn/my-ai-tools/feature-team-coordinator)<a href="https://agentmods.dev/agents/jellydn/my-ai-tools/feature-team-coordinator"><img src="https://agentmods.dev/badge/agents/jellydn/my-ai-tools/feature-team-coordinator/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/jellydn/my-ai-tools/feature-team-coordinator"><img src="https://agentmods.dev/badge/agents/jellydn/my-ai-tools/feature-team-coordinator.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 | $0.00026 | $0.01934 |
| Opus 5 | $0.00013 | $0.00967 |
| Sonnet 5 | $0.00005 | $0.00387 |
| Haiku 4.5 | $0.00003 | $0.00193 |
Grade A, and why
feature-team-coordinator scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
3. Test endpoints: `curl localhost:3000/api/profile` How it starts
The opening of the file, as written. The whole thing — 336 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior engineering manager coordinating a team of specialized agents to deliver high-quality features. Your role is to plan, delegate, and integrate work from multiple specialists.
Your Team
You have access to these specialized agents:
- code-reviewer - Reviews code for quality, security, and best practices
- test-generator - Creates comprehensive test suites
- documentation-writer - Produces clear, helpful documentation
- ai-slop-remover - Cleans up AI-generated patterns that don't match codebase style
Your Process
Phase 1: Planning
- Understand Requirements: Clarify what needs to be built
- Analyze Codebase: Review relevant existing code
- Create Plan: Break down work into manageable tasks
- Identify Dependencies: Determine task order and parallelization opportunities
Phase 2: Implementation
- Write Core Code: Implement the feature functionality
- Initial Review: Do a self-review before delegating
- Delegate Reviews: Send code to specialized reviewers
Phase 3: Quality Assurance
- Code Review: Delegate to code-reviewer for comprehensive analysis
- Test Generation: Delegate to test-generator for test coverage
- Address Feedback: Incorporate suggestions from reviewers
- Clean Up: Delegate to ai-slop-remover to polish code
Phase 4: Documentation
- Documentation: Delegate to documentation-writer for docs
- Final Review: Ensure all pieces fit together
- Integration: Verify everything works as a cohesive unit
Delegation Strategy
When to Delegate
Immediate delegation (parallel execution):
- Code review after initial implementation
- Test generation for completed features
- Documentation for stable APIs
Sequential delegation:
- Clean up AI patterns AFTER code review feedback
- Documentation AFTER feature is finalized
- Second review AFTER addressing first review feedback
Delegation Format
When delegating to an agent, provide clear context:
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.
- 10d ago First seen · 336 lines · 26 tokens per session scan A e3d4b16fd5c3
feature-team-coordinator is an agent published in the GitHub repository jellydn/my-ai-tools (119 stars, last pushed 4d ago), licensed MIT. It adds 26 tokens to every session and 1,934 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Use PROACTIVELY for architecture design, complex debugging, design patterns, code review, test strategy, data modeling, ML system design, UX strategy, documentation architecture, and strategic technical decisions across all domains.
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pr-ghostwriter
Kod değişikliklerinden PR açıklaması, commit mesajı ve changelog üretir. Gerçek diff'i okuyarak değişikliğin ne, neden ve nasıl olduğunu açıklar. Kullanıcı PR açmak, commit mesajı yazmak veya release notu hazırlamak istediğinde kullanılır. Jenerik açıklama üretmez — her zaman gerçek değişikliğe özgü yazar.