Borrowing it
Nothing to install: this file belongs to e7nd7r/gnapsis. 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/e7nd7r/gnapsis/main/.claude/skills/build-knowledge-graph/SKILL.mdgit clone --depth 1 https://github.com/e7nd7r/gnapsisWrote 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/e7nd7r/gnapsis/build-knowledge-graph)<a href="https://agentmods.dev/skills/e7nd7r/gnapsis/build-knowledge-graph"><img src="https://agentmods.dev/badge/skills/e7nd7r/gnapsis/build-knowledge-graph/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/e7nd7r/gnapsis/build-knowledge-graph"><img src="https://agentmods.dev/badge/skills/e7nd7r/gnapsis/build-knowledge-graph.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 | $0.00042 | $0.02556 |
| Opus 5.5 | $0.00017 | $0.01022 |
| Sonnet 5.5 | $0.00008 | $0.00511 |
| Haiku 4.5 | $0.00004 | $0.00256 |
Grade A, and why
build-knowledge-graph 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 2mo 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build Knowledge Graph
Reverse engineer the architecture of the codebase and build a comprehensive knowledge graph using the gnapsis MCP tools, following a strict derivation order methodology.
If an argument is provided, focus the analysis on that scope or path. Otherwise, analyze the entire codebase.
CRITICAL RULES
- Always use gnapsis MCP tools to register all nodes and relationships in the knowledge graph. The gnapsis graph is the primary output.
- Proceed through phases, summarize at each phase boundary. Complete each phase fully, then summarize what was done before moving to the next.
- Use best judgment for ambiguity: When you encounter ambiguity about business domains, feature boundaries, or architectural decisions, use your best technical judgment and document your reasoning. Add a note in the entity description when a decision was ambiguous.
- ALWAYS run
analyze_documentbefore creating entities for a source file. This gives you the exact LSP symbol names. Never guess symbol names. - Use
ref_type: "code"withlsp_symbolfor source files. Only useref_type: "text"for markdown, docs, and config files. - Be exhaustive, not superficial. Scan every directory, every configuration file, every module entry point. Leave no stone unturned.
GNAPSIS TOOL REFERENCE
Initialization & Discovery
init_project— Initialize the database schema (run once at start)project_overview— Get current ontology: taxonomy (categories by scope), entity hierarchy, statistics
Entity Lifecycle
create_entity(name, description, category_ids, parent_ids, commands)— Create an entity with at least one referenceupdate_entity(entity_id, ...)— Update entity, add/remove references, create relationshipsdelete_entity(entity_id)— Delete entity (must have no children)
Taxonomy
create_category(name, scope)— Create a new category at a scope (if the defaults don't fit)
Querying
get_entity(entity_id)— Full entity details with references and relationshipsfind_entities(scope, category, parent_id)— Filter entities by scope/category/parentsearch(query)— Semantic search across entities and referencesquery(entity_id, semantic_query)— Extract relevant subgraph within token budgetget_document_entities(document_path)— Get all entities referenced in a file
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.
- 2mo ago First seen · 234 lines · 42 tokens per session scan A 6c36058aaddb
build-knowledge-graph is a skill published in the GitHub repository e7nd7r/gnapsis (5 stars, last pushed 8mo ago), licensed MIT. It adds 42 tokens to every session and 2,556 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 2026-08-31.
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