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 lossless-group/lossless-agent-skills --skill graphifygit clone --depth 1 https://github.com/lossless-group/lossless-agent-skillsWrote 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/lossless-group/lossless-agent-skills/graphify)<a href="https://agentmods.dev/skills/lossless-group/lossless-agent-skills/graphify"><img src="https://agentmods.dev/badge/skills/lossless-group/lossless-agent-skills/graphify/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/lossless-group/lossless-agent-skills/graphify"><img src="https://agentmods.dev/badge/skills/lossless-group/lossless-agent-skills/graphify.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 | $0.00076 | $0.10203 |
| Opus 5.5 | $0.00030 | $0.04081 |
| Sonnet 5.5 | $0.00015 | $0.02041 |
| Haiku 4.5 | $0.00008 | $0.01020 |
Grade B, and why
graphify scanned grade B with 1 finding 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 10d 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.
Unrestricted tool accessmediumExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
If the user invoked `/graphify --help` or `/graphify -h` (with no other arguments), print the contents of the `## Usage` section above verbatim and stop. Do not run any commands, do not detect files, do not default the p This is a copy
100% identical to graphify — 54 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 703 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/graphify
Turn any folder of files into a navigable knowledge graph with community detection, an honest audit trail, and three outputs: interactive HTML, GraphRAG-ready JSON, and a plain-language GRAPH_REPORT.md.
Usage
/graphify # full pipeline on current directory (HTML viz; add --obsidian for a vault)
/graphify <path> # full pipeline on specific path
/graphify https://github.com/<owner>/<repo> # clone repo then run full pipeline on it
/graphify https://github.com/<owner>/<repo> --branch <branch> # clone a specific branch
/graphify <url1> <url2> ... # clone multiple repos, build each, merge into one cross-repo graph
/graphify <path> --mode deep # thorough extraction, richer INFERRED edges
/graphify <path> --update # incremental - re-extract only new/changed files
/graphify <path> --directed # build directed graph (preserves edge direction: source→target)
/graphify <path> --whisper-model medium # use a larger Whisper model for better transcription accuracy
/graphify <path> --cluster-only # rerun clustering on existing graph
/graphify <path> --no-viz # skip visualization, just report + JSON
/graphify <path> --html # (HTML is generated by default - this flag is a no-op)
/graphify <path> --svg # also export graph.svg (embeds in Notion, GitHub)
/graphify <path> --graphml # export graph.graphml (Gephi, yEd)
/graphify <path> --neo4j # generate graphify-out/cypher.txt for Neo4j
/graphify <path> --neo4j-push bolt://localhost:7687 # push directly to Neo4j
/graphify <path> --falkordb # generate graphify-out/cypher.txt for FalkorDB
/graphify <path> --falkordb-push falkordb://localhost:6379 # push directly to FalkorDB
/graphify <path> --mcp # start MCP stdio server for agent access
/graphify <path> --watch # watch folder, auto-rebuild on code changes (no LLM needed)
/graphify <path> --wiki # build agent-crawlable wiki (index.md + one article per community)
/graphify <path> --obsidian --obsidian-dir ~/vaults/my-project # write vault to custom path (e.g. existing vault)
/graphify add <url> # fetch URL, save to ./raw, update graph
/graphify add <url> --author "Name" # tag who wrote it
/graphify add <url> --contributor "Name" # tag who added it to the corpus
/graphify query "<question>" # BFS traversal - broad context
/graphify query "<question>" --dfs # DFS - trace a specific path
/graphify query "<question>" --budget 1500 # cap answer at N tokens
/graphify path "AuthModule" "Database" # shortest path between two concepts
/graphify explain "SwinTransformer" # plain-language explanation of a node
What ships with it
11 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.
- .graphify_version 6 B
- LICENSE 1.0 KB
- references/add-watch.md 2.4 KB
- references/exports.md 3.3 KB
- references/extraction-spec.md 7.8 KB
- references/github-and-merge.md 2.1 KB
- references/hooks.md 1.2 KB
- references/query.md 13 KB
- references/transcribe.md 3.1 KB
- references/update.md 10 KB
- VENDORED.md 1.8 KB
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.
- 10d ago First seen · 703 lines · 76 tokens per session scan B b5c95cae5ba6
graphify is a skill published in the GitHub repository lossless-group/lossless-agent-skills (4 stars, last pushed 2d ago), licensed MIT. It adds 76 tokens to every session and 10,203 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (unrestricted tool access). It is 100% identical to graphify, differing in 54 lines, and is treated as a copy.
Other skills, from other repositories
rag-pipelines
Best practices for Retrieval-Augmented Generation (RAG) pipelines. Use when working with vector databases, embeddings, chunking strategies, semantic search, or hybrid search architectures.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
rag-pipeline
A guide to RAG, a method where a system retrieves relevant passages from an external knowledge base before an AI model writes an answer. It covers splitting documents, searching by meaning or exact words, combining results, and measuring retrieval quality.
rag-patterns
RAG: embeddings, chunking, hybrid search (BM25+vector), reranking, CRAG, multi-hop. Triggers: RAG, embedding, pgvector, Qdrant, Pinecone, Weaviate, reranker, semantic search.
evaluate
Evaluates RAG retrieval and LLM-as-judge metrics (faithfulness, relevancy, context precision). Triggers: measure RAG quality, knowledge gap, RAG eval, golden dataset.
index
Reindexes KB for semantic search via vector store (Qdrant). Triggers: reindex KB, rebuild index, vector reindex, refresh embeddings.