Borrowing it
Nothing to install: this file belongs to sagar-shirwalkar/collibra-atlas. 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/sagar-shirwalkar/collibra-atlas/main/.agents/skills/atlas-creation/SKILL.mdgit clone --depth 1 https://github.com/sagar-shirwalkar/collibra-atlasWrote 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/sagar-shirwalkar/collibra-atlas/atlas-creation)<a href="https://agentmods.dev/skills/sagar-shirwalkar/collibra-atlas/atlas-creation"><img src="https://agentmods.dev/badge/skills/sagar-shirwalkar/collibra-atlas/atlas-creation/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/sagar-shirwalkar/collibra-atlas/atlas-creation"><img src="https://agentmods.dev/badge/skills/sagar-shirwalkar/collibra-atlas/atlas-creation.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 | $0.00117 | $0.06684 |
| Opus 5 | $0.00059 | $0.03342 |
| Sonnet 5 | $0.00023 | $0.01337 |
| Haiku 4.5 | $0.00012 | $0.00668 |
Grade A, and why
atlas-creation 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 11d 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 — 563 lines — stays where its author put it; the contents beside it link to each section on GitHub.
An Atlas is a local-first AI knowledge layer built from a markdown documentation corpus. It exposes two Model Context Protocol (MCP) servers:
-
Filesystem server (
atlas-fs) — deterministic, zero-infra, backed byripgrep. Tools: list publications, list files, read file, full-text search, get release info. No model, no embeddings, no state. Works with any markdown repo. -
RAG server (
atlas-rag) — semantic search over precomputed embeddings. Tools: search_docs, search_code, get_chunk, get_bundle_info. Loads a portable bundle once at startup; answers queries via single matrix multiply. Supports MLX (Apple Silicon), ONNX+CUDA (NVIDIA), ONNX+CPU (portable floor).
The bundle is built once by the maintainer (atlas-build), distributed as a single artifact, and consumed by end users with zero embedding/chunking/model work.
This skill is platform-agnostic. Corpus-specific configs (repo URLs, branch names, model IDs) live in separate skills.
Advanced Retrieval Techniques (optional, build-time only)
The base Atlas system uses dense embedding search with BM25 hybrid as default. The techniques below improve retrieval quality without adding any LLM calls at query time — all work happens once during bundle creation.
1. Chunk Overlap
Idea: Adjacent H2 sections share a small text overlap (the tail of the previous section prepended to the next) so that queries straddling a section boundary still match. The overlap text is prepended without a special marker — the embedding model treats it as natural context.
Implementation:
atlas/chunk.pyextracts the last 150 chars (_OVERLAP_CHARS) of each H2 section via_section_tail(), word-broken cleanly.chunk_markdown()prepends the tail to the next section before embedding.- Controlled by
overlap_charsparameter (default 150).
Cost profile: Zero at query time. Adds ~150 tokens per chunk boundary at build time (negligible memory/vector storage impact).
2. Hierarchical FS Chunking with Path Metadata
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 563 lines · 117 tokens per session scan A 7c1cee93edfa
atlas-creation is a skill published in the GitHub repository sagar-shirwalkar/collibra-atlas (0 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 117 tokens to every session and 6,684 once invoked, about $0.0006 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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