fastworkflow-diagnostics-and-tooling

fastworkflow-diagnostics-and-tooling is a Runtime diagnostics & profiling skill for AI agents, published by radiantlogicinc in the fastworkflow repository on GitHub. Skillspace rates fastworkflow-diagnostics-and-tooling C (70/100) and it passed the safety scan; the rating is based on static analysis only.

Load this skill when you need to MEASURE a fastWorkflow workflow instead of eyeballing it — inspect ___command_info trained artifacts, check whether cached command snapshots are stale (fingerprint FRESH/STALE), capture or read a turn's command traces / action.jsonl, smoke-test intent-classifier accuracy, configure LOG_LEVEL, inspect the DSPy LLM cache, or hit the server probe endpoints. Trigger phrases/symptoms: "why did it rebuild", "is this workflow trained", "what did the agent actually do", "traces are empty", "stale cache", "threshold.json", "confusion between commands", "cache hit hides the LLM call". Do NOT load for diagnosing a specific failure end-to-end (use fastworkflow-debugging-playbook), for NLU/model theory (use fastworkflow-nlu-pipeline-reference), or for statistics like pass^k variance (use fastworkflow-proof-and-analysis-toolkit).

Grade

C

Quality

70 / 100

Safety

Passed

GitHub stars

49

Unproven: rated by static analysis only, not yet compared head-to-head.

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