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 skills add nicosuave/memex --skill memex-searchgit clone --depth 1 https://github.com/nicosuave/memexWrote 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/skills/nicosuave/memex/memex-search)<a href="https://agentmods.dev/skills/nicosuave/memex/memex-search"><img src="https://agentmods.dev/badge/skills/nicosuave/memex/memex-search/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/skills/nicosuave/memex/memex-search"><img src="https://agentmods.dev/badge/skills/nicosuave/memex/memex-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00042 | $0.02794 |
| Opus 5 | $0.00021 | $0.01397 |
| Sonnet 5 | $0.00008 | $0.00559 |
| Haiku 4.5 | $0.00004 | $0.00279 |
Grade A, and why
memex-search 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memex Search
Recover the smallest set of source-grounded records that answers the question. For the current task, use native notes/history and the worklog first. Read known Codex or ChatGPT conversations with native conversation tools when available. Use Memex for broader discovery or when those sources are unavailable or insufficient.
Choose the retrieval depth
When Memex MCP tools are available, use them for the same workflow below instead
of shell commands: search, sessions, show, context, session, and hydrate.
MCP search returns compact structured JSON and defaults to session diversity;
CLI search still prefers TOON. Use additional_queries for multiple search views
and machines for search scope; read tools take one machine. Sessions are local
and do not auto-index. Preserve the same identifiers, evidence standards, shared
content budgets, and field/page continuations described below. MCP reads are
always bounded; hydrate takes a requests array instead of a JSONL file.
Silently identify the target fact or episode, repository/source/machine/time scope, exact anchors, and what evidence would be sufficient. For analogous work, also identify the mechanism or task shape; topic similarity alone is insufficient.
| Request | First move |
|---|---|
| Known record or session | Read it directly; skip discovery |
| Recent work or resumption | memex sessions --cwd . --limit 20 --format json; use its resume_cmd |
| Exact path, symbol, error, command, PR, URL, or quoted phrase | Lexical search |
| Uncertain wording with some literal anchors | --mode hybrid |
| Abstract similarity with few literal anchors | --mode semantic |
| Decision, fix, or session narrative | Find an anchor, then reconstruct its surrounding sequence |
| Cross-session comparison | Decompose the information needs and diversify by session |
For a simple lookup, start with one query and one record. For an ambiguous request, use 2–3 distinct query views and inspect the best 1–3 sessions. For synthesis, cover each requested variant or time period. These are starting budgets, not quotas. Stop after two reformulation rounds unless the user requests exhaustive research.
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 Changed · +29 lines b0270784a8b5
- 3d ago Changed · -294 lines · -24 tokens per session 9f9dd5521aa8
- 10d ago First seen · 486 lines · 66 tokens per session scan A 418952e707c1
memex-search is a skill published in the GitHub repository nicosuave/memex (201 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 2,794 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
Hybrid Search Architect
Designs a hybrid retrieval pipeline combining dense vector search and BM25 sparse search with reciprocal rank fusion, and explains when to use each configuration.
rag-eval
Run the retrieval regression gate against the current repo state and report whether a recent change helped, hurt, or held steady.
vector-databases
Vector database engineering covering Pinecone, Weaviate, Chroma, Qdrant, pgvector, and FAISS — including embedding pipeline design, HNSW index parameter tuning, hybrid dense+sparse (BM25) search, metadata filtering, namespace and tenant sharding, and RAG retrieval patterns for production knowledge systems.
graph-retrieval
Exposes graph-based retrieval as a tool capability via querygraph. Reads normalized graph store files, builds a query-relevant subgraph, and returns LLM-friendly semantic triples with replayable evidence metadata.
moss-pikachu
Build and maintain Moss Pikachu, a macOS menu bar semantic file search app using Moss Python SDK (PyPI moss>=1.6.0), FSEvents, SwiftUI, and Pikachu pet animations. Use when working on MossPikachu, mossworker.py, FileMonitor, SearchService, menu bar overlay, or Moss integration in this repository.
knowledge-layer
High-level deployment wrapper over RepoBrain core with graph-first knowledge injection and all-file support. Exposes refreshfilesystem and askfilesystem for building and querying the knowledge graph.