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 skills/ybx-stack/rich-brain-clean-hands/codex-referencenpx skills add ybx-stack/rich-brain-clean-hands --skill codex-referencegit clone --depth 1 https://github.com/ybx-stack/rich-brain-clean-handsWhat 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 | $0.00000 | $0.03265 |
| Opus 5 | $0.00000 | $0.01632 |
| Sonnet 5 | $0.00000 | $0.00653 |
| Haiku 4.5 | $0.00000 | $0.00327 |
Grade A, and why
codex-reference 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 2d 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 — 359 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clean Hands Delegation — Full Reference Implementation
This is the production-grade SKILL file that the rich brain loads when it needs to delegate. It covers the complete decision tree: when to delegate, who pulls data, how to launch, how to wait, how to read results. Paths use <WORKSPACE> / <TARGET> placeholders — adapt to your layout.
The executor is pluggable. This reference uses a CLI executor, but any process that reads a packet and writes a compact result works. See docs/EXECUTORS.md for alternatives.
When to Use
Delegate when the task meets ALL of these criteria:
- Input is deterministic (same input = same output every time)
- Does not require interactive judgment, browser, MCP, or real-time decision making
- Would produce large output that pollutes rich brain context
- Is primarily extraction, formatting, parsing, or batch processing
High-frequency delegation targets
Software reverse engineering:
- Batch extract function signatures, strings, xrefs from disassembly output
- Parse large decompilation output by function granularity
- Symbol table / import-export table extraction and formatting
- YARA rule batch scanning
- Binary diff (patch diffing) between versions
- Large disassembly listing filtering and structuring
Web / JS reverse engineering:
- Webpack/Vite bundle splitting and endpoint extraction
- Batch grep API routes, encryption functions, token logic from JS files
- Source map restoration and batch code analysis
- HAR/traffic file batch processing and classification
- Large response body parsing and normalization
General:
- Log file analysis and pattern extraction
- Configuration audit across multiple files
- Dependency tree analysis
- Large CSV/JSON dataset transformation
DO NOT delegate when
- Browser interaction needed (breakpoints, hooks, CAPTCHA)
- MCP tools needed (IDA MCP, browser automation MCP, Burp MCP)
- Strategy decision needed ("what should we try next?")
- Output is small enough to handle inline (< 50 lines)
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.
- 2d ago First seen · 359 lines · 0 tokens per session scan A 0e9a739e7062
codex-reference is a skill published in the GitHub repository ybx-stack/rich-brain-clean-hands (2 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,265 tokens. 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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