Borrowing it
Nothing to install: this file belongs to houshuang/limbic. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/houshuang/limbic/main/CLAUDE.mdgit clone --depth 1 https://github.com/houshuang/limbicWrote 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/instructions/houshuang/limbic/claude-md)<a href="https://agentmods.dev/instructions/houshuang/limbic/claude-md"><img src="https://agentmods.dev/badge/instructions/houshuang/limbic/claude-md.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 | $0.01933 | $0.01933 |
| Opus 5 | $0.00966 | $0.00966 |
| Sonnet 5 | $0.00387 | $0.00387 |
| Haiku 4.5 | $0.00193 | $0.00193 |
Grade A, and why
limbic CLAUDE.md 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 6d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Limbic — AI Agent Guide
Data curation toolkit: embeddings, search, proposals, and AI-assisted verification.
Three packages: limbic.amygdala (find patterns), limbic.hippocampus (manage changes), limbic.cerebellum (verify correctness).
Rules of Thumb
Always whiten domain-focused corpora
If the corpus is about one domain (education, medicine, politics, performing arts), always use whitening. Without it, raw embeddings compress into a narrow similarity band (0.7–0.9) and downstream clustering/novelty/search all degrade.
model = EmbeddingModel(whiten_epsilon=0.1)
model.fit_whitening(corpus_texts)
Skip whitening only when your corpus spans many unrelated domains.
Always genericize number-heavy text
If texts contain variable amounts, dates, section references, or place names around the same argument, use genericize=True. This prevents "allocate 50M" and "allocate 200M" from being treated as different arguments.
model = EmbeddingModel(genericize=True, whiten_epsilon=0.1)
Clustering thresholds depend on whitening
| Corpus state | Threshold | Why |
|---|---|---|
| Raw embeddings, diverse corpus | 0.70–0.75 | Embeddings already spread |
| Raw embeddings, domain-focused | 0.90+ | Narrow cone, everything looks similar |
| Whitened, homogeneous text (extracted claims) | 0.85 | Very similar surface form |
| Whitened, diverse authorship (op-eds, responses) | 0.70–0.75 | Different writers phrase same argument differently |
If your largest cluster has 50+ members, your threshold is too low or you need whitening. Start at 0.75 post-whitening, then sweep [0.70, 0.75, 0.80, 0.85] on your data. Validated on 27K education claims (0.85), 6.5K political proposals (0.75–0.80), and 1.7K op-ed claims (0.75).
Always validate thresholds before shipping
An initial threshold (even 0.85) can produce false positives in your specific domain. LLM-validate a sample of 50–100 pairs at your chosen threshold before using results downstream. Gemini Flash validation costs ~$0.001/pair.
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.
- 6d ago First seen · 170 lines · 1,933 tokens per session scan A 1d57fd6ffad7
limbic CLAUDE.md is an instructions file published in the GitHub repository houshuang/limbic (3 stars, last pushed 2d ago), licensed MIT. It adds 1,933 tokens to every session, about $0.0097 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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