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 nicholasglazer/gnosis-mcp --skill ingestgit clone --depth 1 https://github.com/nicholasglazer/gnosis-mcpWrote 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/nicholasglazer/gnosis-mcp/ingest)<a href="https://agentmods.dev/skills/nicholasglazer/gnosis-mcp/ingest"><img src="https://agentmods.dev/badge/skills/nicholasglazer/gnosis-mcp/ingest/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/nicholasglazer/gnosis-mcp/ingest"><img src="https://agentmods.dev/badge/skills/nicholasglazer/gnosis-mcp/ingest.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 | $0.00054 | $0.01922 |
| Opus 5.5 | $0.00022 | $0.00769 |
| Sonnet 5.5 | $0.00011 | $0.00384 |
| Haiku 4.5 | $0.00005 | $0.00192 |
Grade A, and why
ingest 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 1mo 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 — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ingest
One skill that covers every way to get content into gnosis-mcp. Routes
based on $ARGUMENTS:
- Path to a directory / file → local file ingest
git <repo>→ git-history ingestcrawl <url>→ web-crawl ingestreingest→ full reset + re-ingest from the default pathprune <path>→ delete DB chunks whose source is gone
Action: $ARGUMENTS
ingest <path> — local files
Default entry point. Handles .md, .txt, .ipynb, .toml, .csv,
.json (+ .rst / .pdf if those extras are installed).
First-time ingest
gnosis-mcp ingest ./docs --embed
--embedruns the bundled ONNX embedder (requires[embeddings]extra). Without it you get keyword-only search — usually enough for dev-doc corpora (see bench-experiments), but--embedcosts nothing on a first ingest and enables hybrid later if you want it.- Incremental: every file's content hash is stored, so re-running only
processes changed files. Use
--forceto re-ingest regardless.
Chunk size
Default is 2000 characters (~600 tokens) — peak of the Feb 2026 sweep on a real dev-docs corpus. Override per-ingest or globally:
# This invocation only
GNOSIS_MCP_CHUNK_SIZE=1500 gnosis-mcp ingest ./docs --embed
# Persistent (put in shell profile)
export GNOSIS_MCP_CHUNK_SIZE=3000 # long-form blogs / ADRs
If you're unsure, run /gnosis:tune to sweep sizes against your own
golden queries.
Reorganized your knowledge folder
Files moved, deleted, renamed. Pick one:
# Safest: re-ingest + drop chunks for files that no longer exist
gnosis-mcp ingest ./docs --embed --prune
# Nuclear: drop everything first, then re-ingest
gnosis-mcp ingest ./docs --embed --wipe
# Preview what prune would delete
gnosis-mcp prune ./docs --dry-run
By default --prune leaves crawled URLs alone (since those don't
correspond to local files). Add --include-crawled if you want those
gone too.
ingest git <repo> — git commit history
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.
- 1mo ago Changed · +1 lines eed4c4fd2743
- 1mo ago First seen · 235 lines · 54 tokens per session scan A c857a6e7bd37
ingest is a skill published in the GitHub repository nicholasglazer/gnosis-mcp (30 stars, last pushed 11d ago), licensed MIT. It adds 54 tokens to every session and 1,922 once invoked, about $0.0002 per session on Opus 5.5. 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
accreta-setup
Set up an accreta knowledge base over a corpus — pick a preset, write the config and a source declaration, generate the constitution, wire up MCP, and verify the first ingest. Use when someone wants an agent-maintained knowledge base with provenance and drift detection over a codebase, a document collection, or a…
llmwiki
Build and use a persistent, citation-traceable knowledge wiki from documents, research papers, notes, or project documentation. Use for llmwiki projects, retrieving reusable evidence for an agent task, or compiling a corpus for repeated questions. A one-off file read or live web search does not need a wiki.
karpathy-llm-wiki
Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
memobase
A Russian-language skill for searching a local knowledge base built from documents and other sources such as web pages, YouTube, audio, and Obsidian notes.
kb-memory
Local-first agent memory and knowledge service (RAG) installed from PyPI. Use when a task needs to store or recall long-term user preferences, project decisions, facts, documents or web pages, when different projects need separate memories, or when the user mentions "kb memory", "kb", or a local MCP memory service.…
self_evolve_rag
This document describes how to use the Leeroopedia MCP tools for the Self-Evolving RAG task. It is kept as a reference and is NOT included in the agent prompt.