swmm-modeling-memory git:20260512.d325718A

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---
name: swmm-modeling-memory
description: 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 Modeling Memory

## What this skill provides

- A downstream memory layer for audited Agentic SWMM runs.
- Deterministic summaries that keep evidence, assumptions, repeated QA issues, failures, missing evidence, and run-to-run differences separate.
- Human-readable lessons learned from previous audit records.
- Controlled skill update proposals that require human review and benchmark verification.

This skill does not run SWMM, build SWMM models, modify existing skills, or claim autonomous self-improvement.

Agentic SWMM is not only an automation workflow. It is a memory-informed, verification-first modeling system that can learn from audited modeling history through controlled skill refinement.

## Memory category contract

The skill must preserve these boundaries:

- Evidence: facts found in `experiment_provenance.json`, `comparison.json`, `experiment_note.md`, manifests, QA summaries, SWMM reports, or generated plots.
- Assumptions: choices recorded because a run had missing, ambiguous, fallback, synthetic, or user-provided inputs.
- Lessons learned: reusable observations derived from audited evidence. They are not proof that a new run will pass.
- Recurring failure patterns: repeated missing artifacts, parser failures, QA failures, non-zero SWMM return codes, comparison mismatches, or missing evidence boundaries.
- Skill update proposals: suggested changes to prompts, skills, parsers, workflow order, or documentation. They are not accepted changes and they do not modify runtime behavior.

Never convert assumptions, repeated patterns, or proposals into claims that a model is calibrated, validated, or scientifically ready.

## When to use this skill

Use this skill after `swmm-experiment-audit` has produced run-level artifacts such as:

- `experiment_provenance.json`
- `comparison.json`
- `experiment_note.md`

Use it when:

- multiple audited runs exist,
- the user wants lessons learned across runs,
- the user asks for recurring failure patterns or QA issues,
- the user wants evidence-informed skill refinement proposals.

The proposals may point to relevant workflow skills such as end-to-end orchestration, audit reporting, QA verification, model building, or result parsing. They are not accepted changes.

## Output contract

The script writes these files to the selected modeling-memory output directory:

- `modeling_memory_index.json`
- `modeling_memory_index.md`
- `lessons_learned.md`
- `skill_update_proposals.md`
- `benchmark_verification_plan.md`

The JSON index is the machine-readable source. The Markdown files are human-readable and can be copied to Obsidian with `--obsidian-dir`.

Each output should state that it is derived from audited run artifacts, not raw chat history or external case-study claims. When source audit files are missing, the memory output should preserve that as missing evidence rather than filling gaps.

## CLI

```bash
python3 skills/swmm-modeling-memory/scripts/summarize_memory.py \
  --runs-dir runs \
  --out-dir memory/modeling-memory
```

With optional Obsidian export:

```bash
python3 skills/swmm-modeling-memory/scripts/summarize_memory.py \
  --runs-dir runs \
  --out-dir memory/modeling-memory \
  --obsidian-dir "/path/to/Obsidian/Agentic SWMM/05_Modeling_Memory"
```

## Safety rules

- Read existing audit artifacts only.
- Tolerate partial and failed runs.
- Preserve missing evidence and failed QA as first-class memory signals.
- Do not modify any existing `SKILL.md` files.
- Do not modify benchmark behavior or audit output formats.
- Do not write outside `--out-dir` or the optional `--obsidian-dir`.
- Treat skill update proposals as proposals only.
- Accept real skill refinements only after human review and benchmark verification.
- Do not use external papers, case studies, or undocumented local files as modeling evidence unless they are explicitly referenced in the audited run artifacts.

## Relationship to `swmm-experiment-audit`

`swmm-experiment-audit` records evidence for one run.

`swmm-modeling-memory` reads many audited runs and turns repeated evidence patterns into reusable project memory.

The intended controlled loop is:

1. Run SWMM or attempt a workflow.
2. Audit the run.
3. Preserve an Obsidian-compatible experiment note.
4. Summarize modeling memory across audited runs.
5. Extract recurring failure patterns.
6. Generate a skill update proposal.
7. Review the proposal as a human.
8. Verify with existing benchmarks before accepting any skill change.