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 rules/homenshum/nodebenchai/deep_read_auditgit clone --depth 1 https://github.com/HomenShum/NodeBenchAIWhat 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 | $0.00514 |
| Opus 5 | $0.00000 | $0.00257 |
| Sonnet 5 | $0.00000 | $0.00103 |
| Haiku 4.5 | $0.00000 | $0.00051 |
Grade A, and why
deep_read_audit 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.
What it actually says
Deep-Read Audit Protocol
When collocating across tools, prompts, skills, or rules — NEVER edit from a section-level read. Full end-to-end reads only.
Protocol
- Categorize — Group all target files into 4-6 non-overlapping categories by domain
- Parallel subagents — Launch one Explore subagent per category, all simultaneously
- Full reads required — Each subagent reads EVERY file in its category end-to-end. No partial reads, no grep-only
- Numbered findings — Each finding must include:
[N] FILE:LINE— exact location"exact quote of relevant code/text"— verbatim from the fileISSUE:— description of duplication, obscurity, or centralization need
- Wait for all — Do not synthesize until ALL subagents complete
- Synthesize — Produce a single consolidated report with:
- P0 (extract immediately), P1 (fix this sprint), P2 (next sprint)
- Estimated impact per tier
- Proposed module paths for centralized code
What to look for
- Duplicated helper functions across files
- Overlapping tool/rule descriptions that confuse discovery
- Shared patterns that should be centralized (DB setup, ID generation, timestamps, fetch wrappers)
- Inconsistent naming conventions (params, response shapes, error formats)
- Tools/rules that do similar things in different files
- Hardcoded values that should be shared constants
- Stale/deprecated code still callable
- Instructions duplicated across CLAUDE.md, .claude/rules/, .cursor/rules/
Anti-patterns
- Reading only the first 50 lines and inferring the rest
- Grepping for keywords instead of reading full context
- Editing before the audit completes
- Skipping large files (72KB+ agent files are WHERE the duplication hides)
Related rules
analyst_diagnostic— root cause before fixreexamine_process— orchestrator for when/how to re-examinecompletion_traceability— cite back to original request
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 · 46 lines · 0 tokens per session scan A 9bcd6de24eb7
deep_read_audit is a cursor rule published in the GitHub repository HomenShum/NodeBenchAI (14 stars, last pushed 18d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 514 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 2026-08-30.
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cursorrules
ALWAYS start your session by reading AGENTS.md and .memory/wiki/hot.md to get project context before suggesting code or answering questions.