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Verify small graph-theoretic claims using a local Python helper.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
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.