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Verify small graph-theoretic claims using a local Python helper.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Send email over SMTP using only the Python standard library, with plain-text and HTML bodies, file attachments, cc/bcc, reply-to, a dry-run preview, connection verification, and redacted config inspection.
ALWAYS use this skill when the user asks to send, get, retrieve, find, share, add, or search for a paper. This skill manages the user's Zotero library with 10,000+ papers and can retrieve PDFs, create share links, add new papers, and search. Prefer this over getscipapers for any request involving sending/getting/finding papers.
Use before delivering work that incorporated content the agent did not author — fetched web pages, PDFs, retrieved or library documents, tool or subagent output — or that performs an outward-facing or irreversible action. Maps trust boundaries and runs an abuse-case and prompt-injection check, delegating to a fresh-context security reviewer.
Run bounded autonomous research iterations with evidence gates, recovery ledgers, and optional cross-agent handoffs. Use when the user asks to continue research autonomously, run a research loop, integrate autonomous agent loops, or keep improving a research workflow without repeated prompts.
Use for a clarity-only pass that must not change behavior — simplifying, renaming, de-duplicating, or restructuring code, configs, research scripts, or prose. Gates on understanding the target before touching it and re-verifies after each change so behavior stays fixed.
Use in-flight, the moment you are about to let a non-trivial decision stand — a branching or control-flow change, crossing a module/service/agent boundary, an assertion the type system or proof checker cannot see, a high-stakes or irreversible action, or an analytical step a conclusion rests on. Materializes a fresh-context reviewer biased to disprove, while course-correction is still cheap.
| name | graph_verifier |
| description | Verify small graph-theoretic claims using a local Python helper. |
| user-invocable | true |
| disable-model-invocation | true |
| metadata | {"openclaw":{"emoji":"📐","requires":{"bins":["bash"]}}} |
Use this skill when the user asks to sanity-check a finite graph claim, inspect a small construction, or validate a graph encoding.
/tmp/graph_input.json with the graph data.exec: /workspace/skills/graph-verifier/run_graph_verifier.sh --input /tmp/graph_input.json
graph_data: NetworkX node-link JSONedges: list like [[1,2],[2,3]]adjacency: object mapping nodes to neighbor listsexpected: optional expected values such as {"connected": true, "bipartite": false}For heavy computations beyond NetworkX's capabilities, use the SageMath skill instead:
Use this graph-verifier for simple/fast checks: connectivity, bipartiteness, degree sequence, basic properties.