experiment-spec/SKILL.md

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+---
+name: experiment-spec
+description: Write a rigorous experiment spec for a research phase before any code or training runs. Use instead of feature-spec for scientific/ML research projects. Enforces falsifiable hypothesis, quantitative acceptance criteria, baseline comparison, and null-result protocol.
+---
+
+# Experiment Spec
+
+## Overview
+
+An experiment spec is the scientific contract for one research phase or experiment, written before any implementation or training begins. It is the research analogue of `feature-spec` — same discipline, different vocabulary.
+
+**Core principle:** If you cannot write a falsifiable hypothesis with a quantitative acceptance criterion, you do not understand the experiment well enough to run it.
+
+## When to Use
+
+- Starting a new research phase (Phase 0, Phase 1, etc.)
+- Running any experiment that will produce metrics used to make a go/no-go decision
+- Any time the question is "does X work?" rather than "build X"
+- Before touching training code, data, or hyperparameters for a new question
+
+**When NOT to use:**
+- Implementing a specific component whose behaviour is already defined by a phase spec (use `feature-spec` instead)
+- Exploratory data analysis with no hypothesis (use a notebook; note it as EDA)
+- Bug fixes or refactors
+
+## Iron Laws
+
+1. **The hypothesis must be falsifiable.** "JEPA is promising" is not a hypothesis. "JEPA embeddings will achieve silhouette > 0.35 on held-out data" is. If you cannot state conditions under which the hypothesis is false, it is not a hypothesis.
+2. **Acceptance criteria must be quantitative and pre-registered.** Write the number before you run the experiment. Moving the goalposts after seeing results is p-hacking.
+3. **A baseline is mandatory.** Every experiment must compare against at least one simpler baseline. "Better than nothing" is not a baseline.
+4. **A null-result protocol is mandatory.** State what you will conclude and do if the hypothesis is rejected. "Try harder" is not a protocol.
+5. **The training cutoff is sacred.** No post-cutoff data informs any decision in the spec or implementation.
+
+## Spec Template
+
+```markdown
+# Experiment Spec: [Phase N — Short Name]
+
+## Hypothesis
+
+> "[Falsifiable claim]: We believe [X] will produce [Y], measurable by [Z]."
+
+State conditions under which this hypothesis is FALSE.
+
+## Background
+
+Why this experiment? What does it build on? What prior result or decision motivates it?
+(2–4 sentences. Reference DECISIONS.md or brain wing entries where relevant.)
+
+## Design
+
+### Data
+- Source, date range, pairs/assets, features used
+- Train / validation / test split (respect training cutoff)
+
+### Model / method
+- Architecture, configuration, key hyperparameters
+- What is being varied vs. held fixed
+
+### Baseline
+- What simpler method is being compared against?
+- Why is this the right baseline?
+
+### Ablations (if any)
+- What variants will be run to isolate the effect being studied?
+
+## Acceptance Criteria
+
+- [ ] [Primary criterion — quantitative threshold on primary metric]
+- [ ] [Baseline comparison — e.g. "exceeds baseline by >X%"]
+- [ ] [Reproducibility — reruns within ±Y% of reported metric]
+- [ ] [Collapse/sanity check — e.g. "PC1/rolling-HV correlation < 0.85"]
+
+## Out of Scope
+
+What this experiment explicitly does NOT answer, even if related.
+Anything plausibly in scope that is deferred goes here.
+
+## Null Result Protocol
+
+If the primary acceptance criterion is NOT met:
+- What do we conclude?
+- What is the next step? (Investigate X, pivot to Y, terminate programme)
+- What gets written to the brain and results/summaries/?
+
+## Risks
+
+What could go wrong, and how would it be detected?
+At least one risk must be listed.
+```
+
+## Worked Example
+
+```markdown
+# Experiment Spec: Phase 0 — SSL Feasibility Gate
+
+## Hypothesis
+
+> We believe that a masked autoencoder (MAE) trained on FX hourly data will produce
+> latent embeddings that show structural separability by volatility regime without
+> explicit regime labels, measurable by silhouette score > 0.20 on held-out 2023 data.
+
+This hypothesis is FALSE if silhouette score ≤ 0.20 on the held-out evaluation.
+
+## Background
+
+Before investing in JEPA-specific machinery, we need to confirm that SSL-based
+representation learning can find regime structure in FX time-series at all. MAE is
+the simplest SSL baseline — if it cannot find structure, JEPA will not either.
+Added post Full Grill (2026-05-27). See DECISIONS.md: "Phase 0: SSL feasibility gate".
+
+## Design
+
+### Data
+- Source: DUKASCopy, EUR/USD hourly, 2008–2022 (train), 2023 (held-out test)
+- Features: log-return, rolling 20-period HV, VIX (daily interpolated to hourly)
+- Regime label (for evaluation only, not training): rolling 30-day HV percentile,
+  binary high/low threshold at 50th percentile
+
+### Model
+- Masked Autoencoder: 1D temporal masking (mask contiguous 24h window)
+- Encoder: 3-layer 1D CNN + positional encoding
+- Decoder: 2-layer MLP reconstructing masked segment
+- Context window: 120 hours (5 days)
+
+### Baseline
+- PCA on raw feature vectors (same window) — tests whether any dimensionality
+  reduction shows regime structure, not just SSL
+
+### Ablations
+- Masking horizon: K ∈ {8h, 24h, 72h} — does horizon affect embedding quality?
+
+## Acceptance Criteria
+
+- [ ] Silhouette score > 0.20 on held-out 2023 data (k-means, k=3, vs. HV regime label)
+- [ ] MAE silhouette exceeds PCA baseline silhouette
+- [ ] Rerun within ±10% of reported silhouette
+- [ ] PC1 / rolling-HV correlation < 0.95 (not purely encoding volatility level)
+
+## Out of Scope
+
+- JEPA implementation (Phase 1)
+- Multi-pair training (Phase 1+)
+- VaR or ES computation
+- Any use of post-2023 data
+
+## Null Result Protocol
+
+If silhouette ≤ 0.20:
+- Conclude: SSL cannot reliably find regime structure in EUR/USD hourly data with
+  these features at this resolution
+- Next step: investigate whether (a) hourly resolution is too noisy (try daily),
+  (b) 3 features are insufficient, or (c) regime label definition is too coarse
+- Record result in results/summaries/phase-0-null.md and brain wing jepa-fx/failures/
+
+## Risks
+
+- Encoder collapses to near-constant output: detect via reconstruction loss plateau
+  in first 10 epochs; mitigation: add batch norm, reduce learning rate
+- Regime label too coarse (binary HV): silhouette may be low even with good structure;
+  mitigation: also evaluate with 4-class label (HV quartiles)
+```
+
+## Common Failure Modes
+
+| Failure mode | What it looks like | Fix |
+|---|---|---|
+| Non-falsifiable hypothesis | "JEPA shows promise" | Rewrite with a number |
+| Post-hoc criteria | Threshold chosen after seeing results | Write the number first, commit the spec |
+| No baseline | Silhouette of 0.30 sounds good until PCA achieves 0.35 | Always include a dumber method |
+| Missing null protocol | "We'll figure it out if it fails" | Write it now — it forces clarity about what you're actually betting on |
+| Cutoff violation | Architecture choice informed by 2024 data patterns | Never open the test set during development |
+
+## Brain MCP Integration
+
+**At spec start:**
+- `brain_query wing=jepa-fx hall=decisions` — load current architectural decisions
+- `brain_query wing=jepa-fx hall=failures` — load known failure modes; address them in Risks section
+
+**After spec is approved:**
+- `brain_write` to `jepa-fx/hypotheses/` with the hypothesis and acceptance criteria
+
+**After experiment concludes:**
+- `brain_write` to `jepa-fx/failures/` with any new failure modes discovered
+- `session_log` with outcome
+
+## Cross-References
+
+- Use `feature-spec` for implementing a specific component within an already-specced phase
+- Use `grill-me` on the spec before running the experiment if the hypothesis feels shaky
+- Use `tdd` once the spec is approved — each acceptance criterion maps to a test
+- Use `session-retrospective` after the experiment concludes