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Control Postrun Retro

skill-mr-fang-vlsi-edagent-control-postrun-retro · by Mr-Fang-VLSI

Perform post-experiment retrospective for EDA runs, classify failure/success mechanisms, propose high-confidence next actions, and decide whether to recursively trigger a new workflow-scoped-execution iteration. Use after each experiment batch with monitor/summary/manifest artifacts.

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Install

$ agentstack add skill-mr-fang-vlsi-edagent-control-postrun-retro

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Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

Control Postrun Retro

Overview

Use this skill immediately after an experiment batch finishes. It converts raw artifacts into a structured "what happened / why / what next" decision and gates whether a recursive next run is justified. It does not own the durable experiment-experience registry; consume that through eda-experiment-phenomenology-analyst.

Inputs

Minimum required:

  1. run summary markdown (*.summary.md)
  2. monitor/history (*.monitor.md, *.monitor.history.tsv) or manifest (*.tsv)
  3. key report sources (postCTS/postRoute summary, DRV/type/length distributions, gate outputs)

Optional but preferred:

  1. knowledge-base references used for this batch
  2. web/primary-source references for unstable assumptions
  3. structured evidence-lift artifacts from eda-experiment-phenomenology-analyst:
  • *.results.tsv/json
  • *.conclusion.md
  • *.experience_delta.md

Workflow

  1. Consolidate observations
  • prefer the structured result layer when available; only re-open raw logs to resolve contradictions.
  • extract the smallest set of outcome metrics:
  • timing (WNS/TNS/ViolPaths)
  • PPA (power/area/runtime)
  • geometry/routing (backside usage, type/length distribution, long-net capture)
  • gate status (H1/H2/H3 or equivalent)
  1. Diagnose mechanism, not symptoms
  • assign one primary mechanism and optional secondary mechanisms:
  • model_mismatch
  • flow_policy_mismatch
  • resource_capacity_conflict
  • tool_stability_issue
  • insufficient_evidence
  1. Score improvement proposals
  • for each candidate change, score:
  • confidence (low/medium/high)
  • impact (low/medium/high)
  • cost (low/medium/high)
  • risk (low/medium/high)
  1. Decide recursion
  • trigger recursive workflow-scoped-execution only when:
  • confidence >= medium
  • expected impact >= medium
  • risk is acceptable
  • evidence gap is not blocking
  • otherwise, request focused data collection first.
  1. Emit retrospective artifact
  • write one markdown summary under slurm_logs/04_delay_modeling/:
  • .retro.md
  • include:
  • hypothesis validation state
  • chosen next step
  • explicit stop condition for the next iteration
  • which new or reinforced experience items were relied on

References

Load as needed:

  1. references/retro-checklist.md
  2. references/recursive-loop-policy.md

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.