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/zachjxyz/jvnWrote 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/commands/zachjxyz/jvn/report)<a href="https://agentmods.dev/commands/zachjxyz/jvn/report"><img src="https://agentmods.dev/badge/commands/zachjxyz/jvn/report.svg" alt="Measured on agentmods" 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 | $0.00009 | $0.00811 |
| Opus 5 | $0.00005 | $0.00405 |
| Sonnet 5 | $0.00002 | $0.00162 |
| Haiku 4.5 | $0.00001 | $0.00081 |
Grade A, and why
report 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 7d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/report — Project Analysis Report
Generate a comprehensive analysis of this project. Write the report to a timestamped markdown file.
Process
1. Scan the Project
Read and analyze:
pyproject.tomlorrequirements.txt— dependencies, versions, scriptsCLAUDE.md— project instructions and stack declaration.specify/memory/constitution.md— project principles (if exists)specs/— feature specifications and plans (if any exist)src/directory — modules, routes, models, pipelinesalembic/— database migrations and schema historysrc/models/db/orsrc/schemas/— data model definitionstests/— test files, coverage, and patterns.env.exampleor.env.local— environment variables (never read actual .env)Dockerfileor deployment configs — if present
2. Generate Report
Create a report with these sections:
Executive Summary
2-3 sentences: what this project is, its current state (early/active/mature), and overall health assessment.
Tech Stack
| Package | Version | Purpose | Status |
|---|---|---|---|
| fastapi | 0.x | API framework | Current |
| sqlalchemy | 2.x | Database ORM | Current |
| pytorch | 2.x | ML framework | Current |
| ... | ... | ... | ... |
Status: Current / Outdated / Deprecated / Unknown
Check actual installed versions from pyproject.toml or requirements.txt, not assumptions.
API Endpoints
| Route | Purpose | Auth Required | Method |
|---|---|---|---|
| / | Health check | No | GET |
| /api/v1/predict | Model inference | Yes | POST |
| /api/v1/data | Data ingestion | Yes | POST |
| ... | ... | ... | ... |
Scan the src/api/routes/ directory structure to find all endpoints. Note which ones require authentication.
Data & ML Overview
- Database models: List SQLAlchemy models and their relationships
- ML models: List model definitions, input/output shapes, serving endpoints
- Pipelines: Data processing and training pipelines
- Migrations: Number of migrations, latest migration description
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.
- 7d ago First seen · 98 lines · 9 tokens per session scan A 7cf67e1d666a
report is a command published in the GitHub repository zachjxyz/jvn (2 stars, last pushed 6mo ago), licensed MIT. It adds 9 tokens to every session and 811 once invoked, about $0.0000 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 commands, from other repositories
fix-issue
Command "fix-issue" from KhaiTrang1995/claude-code-structure, covering /project:fix-issue – fix issue command, usage, description, shell steps and run tests after fix.
review
Command "review" from KhaiTrang1995/claude-code-structure, covering /project:review – code review command, usage, description, output format and shell step.
audit
Audit a project's Claude Code adoption and show the score with recommendations.
fix
Fix this project's missing Claude Code setup by writing real CLAUDE.md content, skills, and subagents.
generate-agents
Generate a few subagents matched to this project's detected language(s).
checklist
Generate a custom checklist for the current feature based on user requirements.