Initial commit — design docs + Phase 0 validation harness

Sets up the Socrata project repo with:

docs/ — strategy and design documents
  - idea.md: full product vision
  - implementation-plan.md: Phase 0 + Phase 1 MVP plan
  - phase-0-validation.md: 2-week validation experiment strategy
  - phase-0-plan.md: concrete Phase 0 build plan
  - phase-0-results.md: Phase 0 gate outcome — GO for MVP
  - sysml-modeling.md: metamodel + SE discipline + validation rules
  - socrates.md: agent character, surfaces, modes, prompts, lifecycle
  - sync.md: bidirectional text↔diagram sync engineering
  - design-source/: HTML/CSS/JS handoff bundle from Claude Design

phase-0/ — validated harness (CLI, no UI, no DB)
  - LM Studio (local OpenAI-compatible) generation + detection + judge
  - PlantUML rendering for SysML model visualization
  - 10-seed corpus (8 working + 2 holdouts)
  - 5 corpus runs with iteration history in reports/
  - Final gate: 10/10 pass, mean 4.32/5, holdouts validated

Phase 1 MVP scope and milestones documented in implementation-plan.md.

phase-0/reports/notes-run-02.md

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+# Notes — first scored corpus run (2026-04-28, run 02)
+
+Detailed report: `reports/2026-04-28-1942-run.md`
+
+## Result vs. rubric
+
+**Threshold per [phase-0-validation.md §6.3](../../docs/phase-0-validation.md):**
+- Per-seed: average ≥ 3.5 AND no dimension < 3.0
+- Across corpus: ≥ 80% of seeds pass
+
+| Seed | Avg | Min | Status |
+|---|---|---|---|
+| 01-aristotle | 4.14 | 3 | ✓ |
+| 02-habit-coach | **4.57** | 4 | ✓ |
+| 03-redline-ai | 4.43 | 4 | ✓ |
+| 04-skillswap | 4.43 | 4 | ✓ |
+| 05-cyclist-thing | 4.00 | 3 | ✓ (vague-seed handled correctly) |
+| 06-eventstream | 3.43 | **2** | ✗ |
+| 07-pet-translator | 4.14 | 3 | ✓ |
+| 08-quiet-hours | 4.14 | 3 | ✓ |
+
+**Pass rate: 7 / 8 = 87.5%** — above the 80% threshold.
+
+**Corpus mean across all seeds: 4.16 / 5.**
+
+## Per-dimension averages
+
+Across the 8 seeds:
+
+- modelCoverage: 3.75
+- modelAccuracy: 3.50
+- modelParsimony: 4.50
+- constraintCapture: 4.00
+- assumptionDetectionQuality: 4.13
+- riskDetectionQuality: 4.50
+- voiceAndCharacter: 4.13
+
+The weakest dimension is **modelAccuracy** (3.50) — pulled down by the eventstream 2 and lower scores on other technical/abstract seeds. **modelParsimony** and **riskDetectionQuality** lead at 4.50 — Gemma is doing well at not over-populating and at surfacing substantive risks.
+
+## The eventstream failure
+
+Eventstream produced **5 blocks but 0 associations / 0 constraints / 0 requirements**. The judge correctly penalized:
+- modelAccuracy 2/5 — "lists entities but provides no associations"
+- constraintCapture 2/5 — "failed to capture critical operational constraints"
+
+This is a stochastic regression — the previous (unscored) run on the same seed had `5b 4a 3c 4r`. Two iteration paths:
+1. **Retry to confirm stochasticity.** Re-run the seed alone; if it produces full output, it's variance.
+2. **Strengthen the prompt.** Add explicit guidance: "Even for technical/protocol seeds, you MUST populate all four arrays. If you can't infer associations, prefer fewer high-confidence ones over none."
+
+Recommend (1) first, then (2) if the regression repeats.
+
+## Confidence calibration check
+
+Self-reported `overallConfidence` from the model:
+
+| Seed | Self-conf | Judge avg |
+|---|---|---|
+| 01-aristotle | 0.90 | 4.14 |
+| 02-habit-coach | 0.90 | 4.57 |
+| 03-redline-ai | 0.90 | 4.43 |
+| 04-skillswap | 0.90 | 4.43 |
+| 05-cyclist-thing | **0.30** | 4.00 |
+| 06-eventstream | 0.92 | 3.43 |
+| 07-pet-translator | 0.90 | 4.14 |
+| 08-quiet-hours | 0.90 | 4.14 |
+
+Note **eventstream self-rated 0.92 but actually scored worst** — model overconfident on its weakest output. This is a known small-model failure mode. Worth probing in iteration: tighten the confidence rule to penalize self-rating high when several arrays are empty.
+
+The cyclist-thing low confidence (0.30) correlates correctly with the judge's high score on it — both recognize the seed forced sparse-but-correct output, not low quality.
+
+## Wins worth keeping
+
+1. **Vague-seed handling proven twice.** Cyclist Thing scored 4.00 (passes) — the "under-suggest, push back, refuse to fabricate" prompt instruction works.
+2. **Constraint dimension averages 4.00** — the schema-tightening that removed `kind:'constraint'` from blocks is paying off.
+3. **Risk detection averages 4.50** — the prompt asking for "specific failure modes tied to specific elements" is producing substantive risks across all seed types.
+4. **No structural lint warnings on any seed.** Validation rules hold.
+
+## Iteration candidates (priority order for Day 7)
+
+1. **Retry eventstream.** Cheap — confirms stochastic vs. systemic.
+2. **Add "all four arrays must be populated" guidance to `generate.md`** — only if (1) confirms it's systemic.
+3. **Tighten confidence calibration:** instruct the model to rate confidence based on its OWN model coverage, not on the seed's apparent richness. (e.g., "If you generated 0 associations, your overall confidence cannot exceed 0.5.")
+4. **Habit Coach style as gold standard.** It scored highest — use its prompts/output as a reference for what "good" looks like in further refinements.
+
+## Open questions
+
+- **Should we automate stochasticity testing?** Run each seed N times and report variance? Useful but expensive (~7 min per seed × 8 seeds × 3 runs = ~3 hours).
+- **Judge sycophancy risk.** Gemma is judging Gemma. Worth running a known-bad model (deliberately broken Aristotle) to see if the judge correctly scores it low. Could be a Day 7 sanity check.
+- **Token cost summary in reports.** Currently we don't sum totals. Trivial to add.
+
+## Next session — Day 7
+
+1. Retry eventstream alone, confirm whether the regression repeats
+2. If systemic: iterate `generate.md` on the populate-all-arrays rule and re-run the corpus
+3. If stochastic: just note it and proceed — single-seed variance is real
+4. Run a deliberate-bad sanity check on the judge (mutate Aristotle to remove 80% of elements, see if score drops)