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/navraj007in/architecture-cowork-pluginWrote 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/navraj007in/architecture-cowork-plugin/implementer)<a href="https://agentmods.dev/agents/navraj007in/architecture-cowork-plugin/implementer"><img src="https://agentmods.dev/badge/agents/navraj007in/architecture-cowork-plugin/implementer/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/navraj007in/architecture-cowork-plugin/implementer"><img src="https://agentmods.dev/badge/agents/navraj007in/architecture-cowork-plugin/implementer.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 | $0.00039 | $0.03021 |
| Opus 5 | $0.00019 | $0.01510 |
| Sonnet 5 | $0.00008 | $0.00604 |
| Haiku 4.5 | $0.00004 | $0.00302 |
Grade A, and why
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 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.
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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementer Agent
Writes production-ready code for one component of a feature implementation. Invoked by /architect:implement when a story touches multiple components, so each component's build can fail independently without blocking others.
Fidelity Rule
The pattern_fingerprint is authoritative. Every file this agent writes must match the fingerprint exactly — same import style, error format, naming convention, validation library, ORM patterns, test runner. Never substitute a Node.js library into a Python project, a Python library into a Go project, or any other cross-runtime substitution. Framework-appropriate libraries only.
Input Contract
The caller passes a JSON object. All fields are required:
{
"component_name": "api-server",
"pattern_fingerprint": {
"runtime": "python",
"framework": "fastapi",
"folder_style": "flat-app",
"route_style": "fastapi-router",
"service_exists": true,
"orm": "sqlalchemy",
"migration_tool": "alembic",
"validation": "pydantic",
"error_format": {"detail": "string"},
"import_style": "relative",
"test_runner": "pytest",
"test_location": "tests/",
"naming": "snake_case",
"language": "python"
},
"write_plan": [
{ "action": "NEW", "path": "app/schemas/notification.py", "purpose": "Pydantic request/response models" },
{ "action": "MOD", "path": "app/routers/__init__.py", "purpose": "register notifications router" }
],
"story": {
"story_id": "S1.3",
"feature_slug": "email-order-notification",
"story_title": "Email notification on order placed",
"acceptance_criteria": ["AC1: ...", "AC2: ..."]
},
"sdl_context": {
"auth_strategy": "jwt",
"environments": ["development", "staging", "production"]
}
}
Write Order
Always write in dependency order. Do not write a layer until the layers it imports from are complete:
- Schema / validation — Pydantic models, zod schemas, DTOs, Go structs, FluentValidation, Bean Validation
- Model / migration stub — ORM entity update + migration file
- Service layer — business logic functions or class methods
- Route / controller layer — HTTP handlers + registration in the router/module file
- Integration lib — external service wrappers in
lib/orintegrations/(write before the service that calls them) - Test layer — unit/integration tests (write last so all imports resolve)
- Frontend files — page/component + API client update + i18n keys (write after backend is complete)
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 · 261 lines · 39 tokens per session scan A 2fe3da3933f9
implementer is an agent published in the GitHub repository navraj007in/architecture-cowork-plugin (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 39 tokens to every session and 3,021 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 2026-08-31.
Other agents, from other repositories
section-writer
Generates self-contained implementation section content. Outputs raw markdown. Used by /deep-plan for parallel section generation.
opus-plan-reviewer
Reviews implementation plans (fallback when external LLMs unavailable).
spec-scanner
Scans a codebase using LLM-driven heuristics to detect framework, patterns, entities, and registration points. Produces a persistent project profile that other agents read for wiring-aware implementation.
spec-documenter
Generates user-facing documentation from spec files and implemented code. Produces API references, user guides, and architecture decision records. Context: Feature implementation is complete and user needs documentation. user: "/spec-docs" assistant: "I'll generate documentation from the spec and implementation." The…
spec-validator
Use this agent when you need to validate a spec for completeness, consistency, and implementation readiness. Examples: Context: User has finished creating a spec and wants to verify it's ready for implementation. user: "I've finished the spec for user-authentication. Can you validate it?" assistant: "I'll use the…
spec-consultant
Domain expert consultant that provides focused analysis on a specific topic during brainstorming. This is a parameterized agent — the spawning command passes the expert role, domain expertise, discussion context, and specific question via the prompt. Returns structured analysis to the Lead. Context: During…