diff --git a/.claude/commands/research-strategy.md b/.claude/commands/research-strategy.md
new file mode 100644
index 00000000..19988f74
--- /dev/null
+++ b/.claude/commands/research-strategy.md
@@ -0,0 +1,176 @@
+# /research-strategy
+
+Research new trading strategies or scanner improvements, evaluate fit against
+the existing pipeline, write findings to `docs/iterations/research/`, and
+implement the top-ranked finding as a new scanner if it qualifies.
+
+Usage:
+- `/research-strategy` — autonomous mode: Claude picks research topics
+- `/research-strategy "topic"` — directed mode: research a specific strategy
+
+In CI (`CI=true`), stop before git operations — the workflow handles them.
+
+---
+
+## Step 1: Set Research Agenda
+
+**If a topic argument was provided** (`$ARGUMENTS` is not empty):
+- Research topic = `$ARGUMENTS`
+- Skip to Step 2.
+
+**Autonomous mode** (no argument):
+- Read `docs/iterations/LEARNINGS.md` and all scanner domain files in
+  `docs/iterations/scanners/`
+- Identify the 3-5 highest-leverage research opportunities:
+  - Scanners with low-confidence current understanding
+  - Pending hypotheses marked with `- [ ]`
+  - Gaps: signal types with no current scanner (e.g. dark pool flow,
+    short interest changes, institutional 13F filings)
+- Rank by potential impact. Pick the top topic to research this run.
+- Print your agenda: "Researching: <topic> — Reason: <why this was prioritized>"
+
+## Step 2: Search
+
+Search the following sources for the research topic. For each source, look for:
+signal definition, entry/exit criteria, known statistical edge, known failure
+modes, data requirements.
+
+**Sources to search:**
+- Reddit: r/algotrading, r/quant, r/investing (site:reddit.com)
+- QuantifiedStrategies (site:quantifiedstrategies.com)
+- Alpha Architect (site:alphaarchitect.com)
+- CSS Analytics (site:cssanalytics.wordpress.com)
+- Hacker News: search hn.algolia.com for the topic
+- GitHub: search for "quant scanner <topic>" and "trading strategy <topic>"
+- SSRN: search quantitative finance papers on the topic
+- arXiv q-fin section
+
+Use WebSearch and WebFetch to retrieve actual content. Read at least 3-5
+distinct sources before forming a conclusion.
+
+## Step 3: Cross-Reference Existing Knowledge
+
+Check `docs/iterations/scanners/` and `docs/iterations/research/` for any
+prior work on this topic. Flag explicitly if this overlaps with:
+- An existing scanner (name it and the file)
+- A previously researched and discarded approach (cite the research file)
+- A pending hypothesis in an existing scanner file (cite it)
+
+## Step 4: Evaluate Fit
+
+Score the finding on four dimensions (each: ✅ pass / ⚠️ partial / ❌ fail):
+
+1. **Data availability**: Is the required data source already integrated in
+   `tradingagents/dataflows/`? Check for existing API clients there.
+2. **Implementation complexity**: trivial (<2 hours) / moderate (2-8 hours) /
+   large (>8 hours)
+3. **Signal uniqueness**: Low overlap with existing scanners = good.
+   High overlap = flag as redundant.
+4. **Evidence quality**: backtested with statistics / qualitative analysis /
+   anecdotal only
+
+**Auto-implement threshold** (all must pass for autonomous CI implementation):
+- Data availability: ✅ (data source already integrated)
+- Complexity: trivial or moderate
+- Uniqueness: low overlap
+- Evidence: qualitative or better
+
+## Step 5: Write Research File
+
+Save findings to `docs/iterations/research/` using filename format:
+`YYYY-MM-DD-<topic-slug>.md` where topic-slug is the topic lowercased with
+spaces replaced by hyphens.
+
+Use this template:
+
+```
+# Research: <Topic>
+
+**Date:** YYYY-MM-DD
+**Mode:** directed | autonomous
+
+## Summary
+<2-3 sentences on what was found>
+
+## Sources Reviewed
+- <source 1>: <key finding from this source>
+- <source 2>: <key finding from this source>
+...
+
+## Fit Evaluation
+| Dimension | Score | Notes |
+|-----------|-------|-------|
+| Data availability | ✅/⚠️/❌ | ... |
+| Complexity | trivial/moderate/large | ... |
+| Signal uniqueness | low/medium/high overlap | ... |
+| Evidence quality | backtested/qualitative/anecdotal | ... |
+
+## Recommendation
+Implement / Skip / Needs more data — <reason>
+
+## Proposed Scanner Spec (if recommending implementation)
+- **Scanner name:** `<name>`
+- **Data source:** `tradingagents/dataflows/<existing_file>.py`
+- **Signal logic:** <how to detect the signal, specific thresholds>
+- **Priority rules:** CRITICAL if X, HIGH if Y, MEDIUM otherwise
+- **Context format:** "<what to include in the candidate context string>"
+```
+
+Add an entry to `docs/iterations/LEARNINGS.md` under a `## Research` section
+(create the section if it doesn't exist):
+```
+| research/<filename> | research/<filename>.md | YYYY-MM-DD | <one-line summary> |
+```
+
+## Step 6: Implement (if threshold met)
+
+If the finding meets the auto-implement threshold from Step 4:
+
+1. Read `tradingagents/dataflows/discovery/scanner_registry.py` to understand
+   the `@SCANNER_REGISTRY.register()` registration pattern.
+2. Read an existing simple scanner for the code pattern:
+   `tradingagents/dataflows/discovery/scanners/earnings_calendar.py`
+3. Create `tradingagents/dataflows/discovery/scanners/<name>.py` following
+   the same structure:
+   - Class decorated with `@SCANNER_REGISTRY.register()`
+   - `name` and `pipeline` class attributes
+   - `scan(self, state)` method returning `List[Dict]`
+   - Each dict must have keys: `ticker`, `source`, `context`, `priority`
+   - Priority values: `"CRITICAL"`, `"HIGH"`, `"MEDIUM"`, `"LOW"`
+4. Check `tradingagents/dataflows/discovery/scanners/__init__.py` — if it
+   imports scanners explicitly, add an import for the new one.
+
+If threshold is NOT met: write the research file only. Add this note at the
+top of the research file:
+```
+> **Auto-implementation skipped:** <reason — which threshold failed>
+```
+
+## Step 7: Commit (skip if CI=true)
+
+If the environment variable `CI` is set, stop here. The workflow handles git.
+
+Otherwise:
+```bash
+git add docs/iterations/research/ tradingagents/ docs/iterations/LEARNINGS.md
+```
+
+Run `git commit` with a message in the format:
+`research(<topic>): YYYY-MM-DD — <one-sentence summary of finding and action>`
+
+Then check for an existing open PR on branch `research/current`:
+```bash
+EXISTING=$(gh pr list --head research/current --state open --json number --jq '.[0].number // empty')
+```
+
+If one exists: push to that branch and update PR description with new findings.
+If none exists:
+```bash
+git checkout -b research/current
+git push -u origin research/current
+gh pr create \
+  --title "research: new strategy findings — $(date +%Y-%m-%d)" \
+  --body "$(cat docs/iterations/LEARNINGS.md | head -30)" \
+  --label "automated,research" \
+  --base main
+```