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 wyattowalsh/agents --skill docling-graphgit clone --depth 1 https://github.com/wyattowalsh/agentsWrote 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/wyattowalsh/agents/docling-graph)<a href="https://agentmods.dev/skills/wyattowalsh/agents/docling-graph"><img src="https://agentmods.dev/badge/skills/wyattowalsh/agents/docling-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/wyattowalsh/agents/docling-graph"><img src="https://agentmods.dev/badge/skills/wyattowalsh/agents/docling-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 | $0.00055 | $0.03566 |
| Opus 5.5 | $0.00022 | $0.01426 |
| Sonnet 5.5 | $0.00011 | $0.00713 |
| Haiku 4.5 | $0.00006 | $0.00357 |
Grade A, and why
docling-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 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.
How it starts
The opening of the file, as written. The whole thing — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Docling Graph
Use this skill when the task is specifically about Docling Graph: converting documents into typed Pydantic knowledge graphs, reviewing templates, choosing extraction contracts, tuning pipeline configuration, inspecting/debugging extraction runs, and exporting graph outputs.
Do not use it for generic Docling document parsing, vector-only RAG, graph database administration, ontology-only modeling with no Docling Graph run, or unrelated PDF tooling.
Dispatch
Interpret $ARGUMENTS as [mode] [source-or-template-or-question] [options]. If $ARGUMENTS is empty, ask for the minimum missing context instead of assuming a document, provider, or extraction contract.
| $ARGUMENTS pattern | Mode | What to produce |
|---|---|---|
plan [documents/domain] |
Plan | End-to-end workflow plan, template strategy, provider/config choices, validation path |
template [domain] |
Template | Pydantic graph template or review notes with stable IDs and relationship hints |
| `contract [direct | staged | delta] [domain]` |
convert [source] [template] |
Convert | CLI/API run recipe with inputs, provider/model overrides, output paths, and validation |
api [source] [template] |
API | Python integration using PipelineConfig and explicit override fields |
inspect [output-path] |
Inspect | HTML/debug inspection workflow for an existing or planned output directory |
export [output] |
Export | JSON/CSV/Cypher/NetworkX export and post-export checks |
debug [error-or-output] |
Debug | Root-cause path using logs, debug/trace_data.json, stage artifacts, and schema checks |
batch [folder] |
Batch | Scaling plan for many documents, manifests, retries, idempotency, and QA sampling |
| Natural language | Auto-detect | Classify first, then run the matching mode |
| Empty/unclear | Clarify | Ask for the document type, target graph, run surface, and provider constraints |
What ships with it
16 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.
- evals/evals.json 6.4 KB
- references/cli-api-recipes.md 3.4 KB
- references/debugging.md 3.0 KB
- references/export-graph-management.md 1.9 KB
- references/pipeline-configuration.md 4.6 KB
- references/template-design.md 3.2 KB
- scripts/asset_toolkit/__init__.py 303 B runs code
- scripts/asset_toolkit/_shared.py 5.6 KB runs code
- scripts/asset_toolkit/common.py 3.0 KB runs code
- scripts/asset_toolkit/package.py 43 KB runs code
- scripts/asset_toolkit/validate_evals.py 11 KB runs code
- scripts/asset_toolkit/validate_hooks.py 13 KB runs code
- scripts/asset_toolkit/validate_skill.py 3.6 KB runs code
- scripts/check-env.py 9.5 KB runs code
- scripts/check.py 1.5 KB runs code
- scripts/lint-template.py 15 KB runs code
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 · 332 lines · 55 tokens per session scan A e88f7156e5c5
docling-graph is a skill published in the GitHub repository wyattowalsh/agents (5 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 3,566 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 2026-09-03.
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