Borrowing it
Nothing to install: this file belongs to Yakoub-ai/neural-memory. 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/Yakoub-ai/neural-memory/main/CLAUDE.mdgit clone --depth 1 https://github.com/Yakoub-ai/neural-memoryWrote 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/yakoub-ai/neural-memory/claude-md)<a href="https://agentmods.dev/instructions/yakoub-ai/neural-memory/claude-md"><img src="https://agentmods.dev/badge/instructions/yakoub-ai/neural-memory/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 | $0.01291 | $0.01291 |
| Opus 5 | $0.00646 | $0.00646 |
| Sonnet 5 | $0.00258 | $0.00258 |
| Haiku 4.5 | $0.00129 | $0.00129 |
Grade A, and why
neural-memory 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 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neural Memory Plugin
Neural Memory is a knowledge graph that maps your codebase into four layered, navigable layers — code, bugs, tasks, and insights — so Claude always has deep context without re-reading files.
Quick Start
- First time? Run
/neural-indexto build the knowledge graph - Search:
/neural-queryto find functions, classes, or concepts - Deep dive:
/neural-inspectto see a node's full context, callers, and callees - Stay fresh:
/neural-updateafter code changes - Check health:
/neural-statusto see index freshness - Save knowledge:
/neural-insightto generate docs from accumulated insights
How It Works
The plugin parses your codebase into a four-layer directed graph:
Codebase layer — AST-parsed nodes, LSP-enriched:
- Nodes: modules, classes, functions, methods, project/directory overviews
- Edges: calls, imports, inheritance, containment
- Languages: Python, JavaScript/TypeScript, Go, Rust, Ruby, PHP, Java, C/C++
Bugs layer — auto-imported from .claude/context-log-gotchas.md or via MCP tool:
- Nodes: bug entries with severity, status, root cause, fix description
- Edges:
RELATES_TOcode nodes by file path
Tasks layer — auto-imported from .claude/context-log-tasks-XX.md or via MCP tool:
- Nodes: phases, tasks with status and priority
- Edges:
PHASE_CONTAINStask,RELATES_TOcode nodes - Task statuses:
pending→in_progress→testing→done(auto-archived)
Insights layer — accumulated technical knowledge across sessions:
- Nodes: design decisions, architecture patterns, performance tradeoffs
- Edges:
RELATES_TOcode nodes - Synthesized into docs via
neural_generate_docs//neural-insight
Embeddings: 138-dim composite (100-dim TF-IDF+SVD content + 38-dim structural graph features) Search: three-phase branch search — seed by cosine similarity → graph expansion → weighted rank Security: Secrets, API keys, and sensitive values are automatically redacted LSP: Pyright/pylsp enrichment for high-importance nodes (type signatures, diagnostics)
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 · 104 lines · 1,291 tokens per session scan A 93b5b5e46741
neural-memory CLAUDE.md is an instructions file published in the GitHub repository Yakoub-ai/neural-memory (1 stars, last pushed 5mo ago), licensed MIT. It adds 1,291 tokens to every session, about $0.0065 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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