Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/managedcode/dotnet-skillsnpx agentmods add skills/managedcode/dotnet-skills/graphify-dotnetWrote 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/managedcode/dotnet-skills/graphify-dotnet)<a href="https://agentmods.dev/skills/managedcode/dotnet-skills/graphify-dotnet"><img src="https://agentmods.dev/badge/skills/managedcode/dotnet-skills/graphify-dotnet/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/managedcode/dotnet-skills/graphify-dotnet"><img src="https://agentmods.dev/badge/skills/managedcode/dotnet-skills/graphify-dotnet.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 | $0.00125 | $0.01766 |
| Opus 5.5 | $0.00050 | $0.00706 |
| Sonnet 5.5 | $0.00025 | $0.00353 |
| Haiku 4.5 | $0.00013 | $0.00177 |
Grade A, and why
graphify-dotnet 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- graphify-dotnet — 92% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
graphify-dotnet
Trigger On
graphify,graphify run,graphify watch,graphify benchmark, orgraphify config- generating
graph.json,graph.html,graph.svg,graph.cypher,GRAPH_REPORT.md,obsidian/, orwiki/ - building onboarding maps, architecture snapshots, or dependency-discovery artifacts from a repository
- choosing between AST-only extraction and AI-enriched semantic extraction
- pushing graph output into Neo4j, Obsidian, wiki docs, or CI artifacts
Workflow
- Confirm the problem is structural discovery, architecture review, onboarding, or graph export. If the user only needs one symbol lookup, one bug fix, or one dependency trace, normal repo search and tests are cheaper than a full graph run.
- Install and verify the tool before doing anything else:
dotnet --version dotnet tool install -g graphify-dotnet graphify --version - Start with a bounded AST-only run so the first output is fast and deterministic:
graphify run ./src --format json,html,report --provider none --verbose - Review outputs in this order:
GRAPH_REPORT.mdfor quick signalgraph.htmlfor visual explorationgraph.jsonfor scripting and downstream tooling
- Add AI enrichment only when inferred relationships or conceptual grouping matter more than strict syntax-only structure.
- Expand export formats for the real consumer:
svgfor static docs and PRsneo4jfor graph queriesobsidian,wikifor knowledge-base or onboarding flows
- Use
watchfor iterative architecture work, but rerun a cleanrunperiodically because deletes and renames can leave stale references behind. - Run
benchmarkonly after you already trust the generatedgraph.json; its value is comparative token-reduction evidence, not billing-grade accounting.
Architecture
flowchart LR
A["Repository or subtree"] --> B["graphify run / watch"]
B --> C{"AI provider configured?"}
C -->|No| D["AST extraction only"]
C -->|Yes| E["AST + semantic extraction"]
D --> F["Knowledge graph + Louvain communities"]
E --> F
F --> G{"Output target"}
G -->|Human review| H["graph.html + GRAPH_REPORT.md"]
G -->|Automation| I["graph.json"]
G -->|Static docs| J["graph.svg"]
G -->|Knowledge base| K["obsidian/ or wiki/"]
G -->|Graph queries| L["graph.cypher for Neo4j"]
What ships with it
3 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.
- 1mo ago First seen · 164 lines · 125 tokens per session scan A 907ccb7c6bc4
graphify-dotnet is a skill published in the GitHub repository managedcode/dotnet-skills (490 stars, last pushed yesterday), licensed MIT. It adds 125 tokens to every session and 1,766 once invoked, about $0.0005 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 2026-09-03.
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