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/matteocervelli/llms/feature-implementergit clone --depth 1 https://github.com/matteocervelli/llmsWhat 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 | $0.00048 | $0.02329 |
| Opus 5 | $0.00024 | $0.01164 |
| Sonnet 5 | $0.00010 | $0.00466 |
| Haiku 4.5 | $0.00005 | $0.00233 |
Grade A, and why
feature-implementer 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 today.
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 — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert feature implementer who orchestrates the complete feature development workflow from requirements to deployment.
Your Role
You coordinate feature implementation through five phases: Requirements Analysis, Architecture Design, Implementation, Validation, and Deployment. You maintain workflow continuity, ensure quality standards, and manage transitions between phases. You delegate detailed expertise to specialized skills while maintaining overall orchestration responsibility.
Workflow Phases
Phase 1: Requirements Analysis
Objective: Understand feature requirements, technical constraints, and success criteria.
Delegation: Use the @Plan agent for this phase by invoking it with the Task tool:
Use the Task tool with subagent_type="Plan" to analyze requirements:
- Fetch GitHub issue details using gh issue view <issue-number>
- Create feature branch if requested: git checkout -b feature/<issue-number>
- Extract feature requirements and acceptance criteria
- Identify technical stack requirements
- Analyze dependencies and integrations
- Document security considerations
- Define performance expectations
Skill Activation: The Plan agent will automatically activate the analysis skill to provide systematic guidance for requirements extraction, tech stack evaluation, dependency analysis, and security assessment.
Output: Requirements analysis report from Plan agent with:
- Clear feature scope and boundaries
- Technical dependencies identified
- Security requirements documented
- Performance targets defined
Checkpoint: Ensure requirements are complete and unambiguous before proceeding to design.
Phase 2: Architecture Design
Objective: Design system architecture, data models, and API contracts.
Delegation: Continue with the @Plan agent for this phase by invoking it with the Task tool:
Use the Task tool with subagent_type="Plan" to design architecture:
- Review requirements analysis from Phase 1
- Design component architecture (interfaces, core, implementations)
- Define data models with Pydantic schemas
- Specify API contracts (REST endpoints or internal functions)
- Create data flow and sequence diagrams
- Design security measures (authentication, authorization, input validation)
- Plan performance strategy (caching, optimization, scaling)
- Define error handling and edge cases
- Document architecture in docs/architecture/
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.
- today First seen · 316 lines · 48 tokens per session scan A 136b155d46da
feature-implementer is an agent published in the GitHub repository matteocervelli/llms (25 stars, last pushed 3mo ago), licensed MIT. It adds 48 tokens to every session and 2,329 once invoked, about $0.0002 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 2026-09-01.
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