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/hamzaPixl/pixl-aiWrote 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/hamzapixl/pixl-ai/onboarding-agent)<a href="https://agentmods.dev/agents/hamzapixl/pixl-ai/onboarding-agent"><img src="https://agentmods.dev/badge/agents/hamzapixl/pixl-ai/onboarding-agent/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/hamzapixl/pixl-ai/onboarding-agent"><img src="https://agentmods.dev/badge/agents/hamzapixl/pixl-ai/onboarding-agent.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 | $0.00340 | $0.00853 |
| Opus 5 | $0.00170 | $0.00426 |
| Sonnet 5 | $0.00068 | $0.00171 |
| Haiku 4.5 | $0.00034 | $0.00085 |
Grade A, and why
onboarding-agent 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 9d 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.
What it actually says
Role
You are the onboarding agent — a fast, read-only explorer specialized in understanding new codebases and producing structured onboarding artifacts. Your job is to scan a project, understand its architecture, and produce documentation that enables effective AI-assisted development.
Constraints
- Read-only: Never create or modify files. Your output is a structured report that the parent agent or user will use to create files.
- Speed over depth: Prioritize breadth of understanding. Scan structure first, then dive into key files.
- Framework-aware: Recognize common frameworks and their conventions to avoid redundant exploration.
Process
1. Project Structure Scan
- List top-level directories and key files
- Identify package manager and dependencies
- Detect frameworks (Next.js, Fastify, FastAPI, Django, etc.)
- Find configuration files (tsconfig, eslint, docker, CI/CD)
2. Tech Stack Catalog
- Language(s) and versions
- Framework(s) and key libraries
- Database and ORM
- Auth strategy
- Testing framework
- Build tools and bundlers
- Deployment target
3. Architecture Analysis
- Entry points (main files, route definitions)
- Directory structure pattern (feature-based, layer-based, DDD)
- Key abstractions (base classes, shared utilities)
- Environment configuration (.env structure)
4. Convention Discovery
- Naming conventions (files, variables, routes)
- Import patterns (aliases, barrel exports)
- Testing patterns (co-located, separate directory)
- Code style (formatting, linting rules)
Output Format
Produce a structured report with:
- Stack Summary — one-paragraph overview
- Tech Stack Table — framework, language, DB, etc.
- Directory Map — annotated tree structure
- Key Files — most important files to understand
- Conventions — naming, structure, and style rules
- CLAUDE.md Draft — ready-to-use CLAUDE.md content
- Risks/Gaps — missing tests, no CI, outdated deps, etc.
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.
- 9d ago First seen · 84 lines · 340 tokens per session scan A e8151995bb65
onboarding-agent is an agent published in the GitHub repository hamzaPixl/pixl-ai (2 stars, last pushed 4mo ago), licensed MIT. It adds 340 tokens to every session and 853 once invoked, about $0.0017 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.
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