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 agentmods add skills/hellothisworld/open-mind/code-graphsnpx skills add HelloThisWorld/open-mind --skill code-graphsgit clone --depth 1 https://github.com/HelloThisWorld/open-mindWhat 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 | $0.00047 | $0.00470 |
| Opus 5 | $0.00023 | $0.00235 |
| Sonnet 5 | $0.00009 | $0.00094 |
| Haiku 4.5 | $0.00005 | $0.00047 |
Grade A, and why
code-graphs 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 yesterday.
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.
What it actually says
Code graphs — deterministic structure map
What it does
Performs line-oriented static analysis (language specs in openmind.langspec,
tree-sitter for Java where available) to recover, for ANY repository, a persisted
structure artifact: a module tree, a per-file definition index, entry points, an
import/dependency graph, and a name-based call/usage graph. From it, openmind.diagrams
projects interactive call graph data plus Mermaid / DOT diagrams.
Determinism & anti-fabrication contract
- Graphs are recovered from facts in the code (defs, imports, call sites) — never model-generated.
- Internal import edges are resolved where the language makes it tractable; unresolved/external references are recorded as such, never invented.
- Call edges are name-based and flagged
ambiguouswhen a symbol is defined in more than one file — the tool states uncertainty instead of guessing. - File nodes and definitions carry source locations; directory/module nodes are rollups over real files. There is no path that fabricates an edge.
- Incremental: hash-keyed per file, so an unchanged file is not re-scanned.
Invocation
- REST:
GET /structure?scope=(overview: stats, entry points, top modules),GET /graph?scope=(call roots),GET /graph/children, andGET /graph/node?scope=&id=(a node's source location, defs, call neighbors, cross-linked glossary terms). - Library:
openmind.structure.build_structure(...),get_definition(doc, symbol),term_usage(doc, term);openmind.diagramsfor projections.
Implementation / tests
openmind/structure.py, openmind/diagrams.py, openmind/langspec.py,
openmind/javaparse.py. Acceptance: tests/verify_structure.py, tests/verify_diagrams.py.
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.
- yesterday First seen · 37 lines · 47 tokens per session scan A a89d4be9875a
code-graphs is a skill published in the GitHub repository HelloThisWorld/open-mind (1 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 470 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 2026-08-31.
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