swmm-experiment-audit
SolidConsolidate Agentic SWMM run artifacts into auditable provenance, comparison records, and local Obsidian audit notes. Use after any SWMM build/run/QA attempt, successful or failed, when an agent or CLI workflow needs a traceable record of inputs, commands, artifacts, metrics, QA checks, run-to-run differences, and first-user-friendly Obsidian visualization.
Install
Quality Score: 78/100
Skill Content
Details
- Author
- Zhonghao1995
- Repository
- Zhonghao1995/agentic-swmm-workflow
- Created
- 5 months ago
- Last Updated
- 3 days ago
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
swmm-modeling-memory
Read historical Agentic SWMM experiment audit artifacts and summarize repeated assumptions, QA issues, failures, missing evidence, run-to-run differences, lessons learned, and controlled skill update proposals. Use downstream of swmm-experiment-audit when multiple audited runs exist or when a user asks for modeling memory, failure-pattern extraction, lessons learned, or human-reviewed skill refinement proposals.
swmm-report
Generate a client-deliverable Word (.docx) report from an audited SWMM run directory. Reads manifest.json, experiment_provenance.json, model_diagnostics.json, comparison.json, and any PNG figures — SWMM is never re-run. Supports custom YAML/JSON section templates.
experiment-audit
Use this skill for scientific and ML-research reasoning work — evaluating experimental claims, auditing training runs or ablations, checking whether a statistical claim holds up, assessing reproducibility, reconciling contradictory results, reviewing a paper's methodology or results section, writing reviewer-style feedback, or producing a structured research report. This is a scientific reasoning discipline, not just a tool wrapper, so it applies even with no live data source — e.g. reviewing a pasted table of results, sanity-checking a claimed effect size, or evaluating an ablation described in prose. Trigger on phrasing like "did I mess up this experiment," "is this result real," "why did my loss/reward do X," "which run is better," "is this ablation confounded," "review this paper's claims," "write reviewer feedback," "is this reproducible," "what should I conclude from this," or "write up these results." When the user's data lives in Weights & Biases, this skill also covers the experiment-audit-mcp integr