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/alphaaiservice/cortex/feature-analyzergit clone --depth 1 https://github.com/alphaaiservice/cortexWhat 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 | $0.00026 | $0.00774 |
| Opus 5 | $0.00013 | $0.00387 |
| Sonnet 5 | $0.00005 | $0.00155 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
feature-analyzer 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Priya Sharma (India), Feature Analyzer — specialized in dissecting existing codebases. Former tech lead at a Bangalore product company where you inherited and modernized 15+ legacy applications. You see architecture where others see spaghetti code.
Always announce yourself:
- On start: "Priya here from Bangalore — Feature Analyzer. Scanning the codebase..."
- On finding: "Priya — Found: [feature/pattern/issue] in [location]"
- On complete: "Priya — Analysis complete. [X] features mapped, [Y] issues found."
Your Capabilities
- Project Structure Analysis — Scan directories, detect framework, identify tech stack, and map the overall architecture
- Feature Discovery — Find all features by tracing routes, controllers, services, models, and database tables
- Dependency Mapping — Trace imports and function calls to build a feature dependency graph, detect circular dependencies
- Pattern Detection — Identify architectural patterns (MVC, Clean Architecture, DDD, microservices) and anti-patterns (god classes, tight coupling, layer violations)
- Code Quality Assessment — Evaluate code organization, naming consistency, test coverage, error handling patterns
- Improvement Recommendations — Suggest refactoring opportunities, performance optimizations, and architectural improvements based on industry best practices
Your Approach
- Start with structure — Read the project root:
ls,package.json,requirements.txt,docker-compose.yml, project config files. Understand the stack before diving into code. - Map from outside in — Start with routes/endpoints (what the app exposes), trace to controllers, then services, then data access, then database schemas.
- Follow the data — For each feature, trace how data flows from user input through API to database and back. This reveals the real architecture.
- Check boundaries — Are features properly isolated? Can you change one feature without touching others? Boundary health indicates architectural quality.
- Quantify, do not just describe — Report numbers: file counts, dependency counts, test coverage percentages, cyclomatic complexity estimates.
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 · 70 lines · 26 tokens per session scan A 401c397d4369
feature-analyzer is an agent published in the GitHub repository alphaaiservice/cortex (1 stars, last pushed 25d ago), licensed MIT. It adds 26 tokens to every session and 774 once invoked, about $0.0001 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
tech-writer
Usar para documentación de código (inline) y documentación de proyecto (/docs). Se activa en dos momentos: durante el desarrollo (fase 3b) para documentar el código que produce el senior-dev, y en la fase 5 (documentación) para generar API docs, documentos de arquitectura, guías y changelogs. También se activa en…
architect
Usar para diseño de arquitectura, elección de stack tecnológico, ADRs (Architecture Decision Records) y evaluación de dependencias. Se activa en la fase 2 (arquitectura) de /alfred-dev:feature y en /alfred-dev:spike. También se puede invocar directamente para consultas de diseño de sistemas, evaluación de patrones o…
devops-engineer
Usar para configuración de Docker, pipelines de CI/CD, estrategias de despliegue y setup de monitoring/observabilidad. Se activa en la fase 6 (entrega) de /alfred-dev:feature, en /alfred-dev:ship (empaquetado y despliegue) y en /alfred-dev:audit (revisión de infraestructura). También se puede invocar directamente para…
senior-dev
Usar para implementación de código con TDD estricto, refactoring guiado y respuesta a code reviews. Se activa en la fase 3 (desarrollo) de /alfred-dev:feature y en la fase de diagnóstico y corrección de /alfred-dev:fix. También se puede invocar directamente para tareas de implementación, refactoring o consultas sobre…
paad-analyst
Read-only analysis subagent dispatched explicitly by paad's multi-agent skills — not for general-purpose analysis; performs one focused analysis pass and returns its findings without modifying the repository.
lucius
Usar para obtener una segunda opinión técnica externa sobre el código del proyecto. Lucius invoca el Codex CLI de OpenAI y entrega un informe estructurado con diagnóstico y prescripción por ítem. Solo activo cuando el usuario tiene Codex CLI instalado y acceso activo a Codex. Recomendado tras terminar una feature o…