使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/emiliodominguez/flow-skills --skill flow-diagnose开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
Diagnose
Reduce uncertainty before changing behavior. A matching prediction supports a hypothesis; confirmation needs evidence that rules out plausible alternatives.
Process
- Establish symptom, expected behavior, environment, revision and impact. Reproduce with a
small input when possible. For intermittent failures, record frequency, logs, traces and conditions instead of waiting for an on-demand repro. Name the check that will prove the eventual fix (a failing regression test or a controlled signal), even before the cause is known.
- State a falsifiable hypothesis per plausible cause, with predictions and evidence for and
against. Keep observations separate from explanations. Inspect actual data/control flow and changes near onset.
- Pick the experiment that discriminates among causes. A stale read alone cannot separate
cache invalidation from replica lag. Use a controlled intervention, boundary trace or counterexample, accounting for confounders and observer effects.
- Instrument the smallest useful boundaries: structured logs, no sensitive payloads, only the
inputs, correlation and timing the question needs. For performance, profile the workload.
- Update each hypothesis: refuted, supported, confirmed or unresolved, in a compact evidence
table. Parallel read-only investigation is fine for independent questions when authorized; agents must not race to patch competing theories.
- Once the cause is supported, make the smallest in-scope correction. Add a regression test
that fails on the defect and passes on the fix where practical; otherwise keep a reproducible trace or controlled acceptance check and state its limits.
- Check the fix, affected behavior and required repository gates. Remove temporary
instrumentation unless intentionally kept. Report root-cause confidence and residual risk.
Stop conditions
Honor any investigation budget. If experiments stop yielding information, access is missing, or evidence cannot separate causes, report the smallest missing observation instead of guessing or repeating a patch. Urgent containment belongs in flow-triage under the user's operational authorization.
Red flags
- "I know what this is": write the competing hypothesis down and test it anyway.
- "Let me try one more change": no new patch without a new observation.
- "It went away": an unexplained disappearance is not a fix.
- "Three fixes failed": the model of the system is wrong; revisit assumptions, not the patch.
Anti-patterns
- Treating correlation or one matching prediction as causation.
- Blocking on deterministic reproduction before collecting better telemetry.
- Stacking speculative fixes so nobody knows which cause mattered.
- Accepting a test/import failure as the regression signal without inspecting it.
Done when
- Cause and correction have discriminating evidence, or the investigation ends with a precise
unresolved hypothesis and the next observation needed.
- The final artifact and relevant checks support the claimed outcome.
- Temporary instrumentation and uncertainty are accounted for.
Use flow-work for implementation or flow-triage when production impact needs containment.
