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/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/commands/yakoub-ai/neural-memory/neural-index)<a href="https://agentmods.dev/commands/yakoub-ai/neural-memory/neural-index"><img src="https://agentmods.dev/badge/commands/yakoub-ai/neural-memory/neural-index/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/commands/yakoub-ai/neural-memory/neural-index"><img src="https://agentmods.dev/badge/commands/yakoub-ai/neural-memory/neural-index.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 | $0.00000 | $0.00299 |
| Opus 5 | $0.00000 | $0.00150 |
| Sonnet 5 | $0.00000 | $0.00060 |
| Haiku 4.5 | $0.00000 | $0.00030 |
Grade A, and why
neural-index 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
Neural Memory — Full Index
Build the complete neural memory knowledge graph for this codebase.
What this does
- Discovers all Python files (respecting exclude patterns)
- Parses AST to extract functions, classes, methods, modules
- Builds a directed graph of call relationships, imports, and inheritance
- Redacts sensitive content (secrets, API keys, connection strings)
- Computes importance scores for each node
- Optionally generates AI-powered summaries for high-importance nodes
How to call
Via MCP tool (neural-memory configured as MCP server in Claude Code):
Tool: neural_index
{ "mode": "both" }
Via Python (working directly in the project):
import asyncio
from neural_memory.server import neural_index, IndexInput
asyncio.run(neural_index(IndexInput(mode="both")))
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
project_root |
str | "." |
Project root directory |
mode |
str | config default | "ast_only" (fast/local), "api_only" (AI summaries), "both" (default) |
First run takes longer. Subsequent runs can use /neural-update for incremental changes.
After indexing, use /neural-query to search and /neural-inspect to deep-dive.
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 · 38 lines · 0 tokens per session scan A 6f844f7d36b6
neural-index is a command published in the GitHub repository Yakoub-ai/neural-memory (1 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 299 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.
Other commands, from other repositories
recall-save
Generate / overwrite .recall/context.md with Recall's local offline summarizer.
learn
Force claude-smart to extract learnings from this session now.
ccr
CCR (Compressed Context with Retrieval) — recall the full original of context that greatcto compressed/filtered out, by its short id. The retrieval half of the compression layer.
ai-act-ask
Answer an EU AI Act question grounded in the bundled knowledge base — verbatim statute text, obligation paraphrases, and the compound-risk taxonomy. Offline and deterministic by default; cites the articles it relies on.
broadcast
Run broadcast on a distilled Raw — update related existing pages conversationally.
check
Is the context layer still true? 0-token staleness + broken-citation audit (no LLM, CI-friendly). Add --status for the fast hash-only subset.