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Eda Experiment Phenomenology Analyst

skill-mr-fang-vlsi-edagent-eda-experiment-phenomenology-analyst · by Mr-Fang-VLSI

Extract and maintain reusable experiment knowledge across `log -> result -> conclusion -> experience` layers, then hand that evidence to execution, retrospective, and veto skills without forcing them to re-read raw logs every time.

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Install

$ agentstack add skill-mr-fang-vlsi-edagent-eda-experiment-phenomenology-analyst

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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

EDA Experiment Phenomenology Analyst

When to use

Use this skill when:

  1. an experiment or batch has finished and raw logs should be lifted into reusable evidence,
  2. repeated experiment patterns need to become durable recommendations instead of one-off notes,
  3. control-postrun-retro or control-theory-veto should consume prior empirical experience without reparsing all prior logs,
  4. a new “experiment expert” style workflow needs access to an experience layer rather than raw batch files only.

Scope Boundary

This skill owns horizontal evidence lifting and experiment-experience maintenance.

It owns:

  • extracting structured facts from raw experiment artifacts,
  • separating result, conclusion, and experience,
  • maintaining experience deltas or registry-style artifacts,
  • surfacing empirically repeated patterns that downstream skills may rely on.

It does not own:

  • final post-run recursion decisions (control-postrun-retro),
  • final veto authority (control-theory-veto),
  • workflow routing ownership,
  • domain-method correctness claims by itself.

Expected Downstream Consumers

Typical consumers:

  • workflow-scoped-execution for batch closeout,
  • control-postrun-retro for next-step decision,
  • control-theory-veto for experience-informed GO/CONDITIONAL/NO-GO,
  • workflow-research-chain for durable experiment memory across stages.

Inputs

Provide or derive:

  1. raw batch artifacts:
  • summaries,
  • manifests,
  • monitor/history files,
  • report files,
  • stdout/stderr logs when needed,
  1. experiment objective and comparison contract,
  2. any existing memory or experience artifacts for the same design/branch,
  3. KB references if the batch conclusion depends on policy interpretation.

Outputs

Emit the smallest set needed by downstream consumers:

  1. result artifact
  • structured metrics and directly observed facts
  • recommended form: *.results.tsv or *.results.json
  1. conclusion artifact
  • batch-local mechanism judgment with cited evidence paths
  • recommended form: *.conclusion.md
  1. experience_delta artifact
  • reusable experience items added or reinforced by this batch
  • recommended form: *.experience_delta.md
  1. optional registry update
  • append/refresh durable experience inventory when the workflow calls for long-lived storage

Hard rules

  1. Do not skip from raw logs directly to durable advice; write result and conclusion first.
  2. Mark speculation as speculation; only experience with repeated or well-supported evidence should influence later veto decisions.
  3. Keep batch-local conclusions separate from cross-batch experience.
  4. When experience is used for veto, include the trigger pattern and linked evidence runs.

Operational References

Load only what is needed:

  1. Load references/layer-contract.md when deciding how to split artifacts across log, result, conclusion, and experience.
  2. Load references/consumer-integration-policy.md when wiring outputs into workflow-scoped-execution, control-postrun-retro, control-theory-veto, or workflow-research-chain.
  3. Use docs/knowledge_base/templates/experiment_results_template.tsv, docs/knowledge_base/templates/experiment_conclusion_template.md, and docs/knowledge_base/templates/experiment_experience_delta_template.md as the default artifact skeletons when no more specific batch-local template already exists.

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.