# Research Flywheel

> Mine conversations for research-worthy topics using LCM search, deep research them via deep-research skill, store findings, and report digests. Closes the loop between conversations and knowledge. Use when the user says 'research flywheel', 'mine conversations', 'find research topics', 'what should we research', or wants to surface insights from past sessions.

- Skill: `cyperx84/research-flywheel` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add cyperx84/research-flywheel`
- Raw SKILL.md: https://api.skillmd.com/api/skills/cyperx84/research-flywheel/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- License: MIT
- Author: cyperx84 (https://skillmd.com/u/cyperx84)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/cyperx84/research-flywheel

---


# Research Flywheel

Mine conversations for topics worth researching, dispatch deep research, store findings, and produce digests. Closes the loop between what Chris talks about and what gets properly researched.

## Trigger

Use when the user says any of:
- "research flywheel"
- "mine conversations for research topics"
- "what should I research"
- "research digest"
- "flywheel status"
- "flywheel run"

## Commands

### `research-flywheel mine` — Find research candidates

Scans conversation history for research-worthy topics.

```bash
research-flywheel mine [--since "2026-03-17"] [--agents all|researcher|claw|builder]
```

**Flow:**
1. Run configurable mine patterns (from `references/mine-patterns.md`) via `lcm_grep`
2. Extract topic mentions with source references (conversation ID + message context)
3. Deduplicate against already-completed research in state
4. Score candidates: frequency × recency × Chris-initiated bonus
5. Store as pending candidates in state file
6. Print ranked list

**Patterns:** See `references/mine-patterns.md`. Configurable, not hardcoded regex. Default patterns look for:
- Questions about topics ("how does X work", "what's the best Y")
- Mentions of tools/products/technologies in context of evaluation
- "research this", "look into", "investigate" directives
- Topics mentioned across multiple conversations (high frequency signal)

### `research-flywheel research "<topic>"` — Dispatch deep research

Wraps the existing `deep-research` skill to research a specific topic.

```bash
research-flywheel research "edge AI for snow sports" --breadth 6 --depth 3
```

**Flow:**
1. Look up topic in pending candidates (or add if new)
2. Delegate to `~/.openclaw/skills/deep-research/scripts/deep-research.mjs` with the topic
3. Monitor progress via state file updates
4. On completion: store report path in state, update candidate status to "completed"
5. Optionally write vault note via `obsidian-cli`

### `research-flywheel status` — Show research state

```bash
research-flywheel status [--topic "snow"]
```

Prints:
- Completed research (topic, report path, date)
- Pending candidates (ranked by score)
- Gaps (topics mentioned but never researched)

### `research-flywheel digest` — Produce summary

```bash
research-flywheel digest [--since "2026-03-17"] [--output discord|vault|stdout]
```

**Flow:**
1. Read all completed reports since the date
2. Generate a concise digest (key findings per topic, 2-3 sentences each)
3. Deliver to specified output channel
4. Discord delivery uses the `message` tool
5. Vault delivery uses `obsidian-cli create`

### `research-flywheel run` — Full cycle

```bash
research-flywheel run [--dry-run] [--auto] [--top N]
```

**Flow:**
1. Mine for candidates
2. Filter to top N (default 3)
3. Research each (unless --dry-run)
4. Generate digest
5. If --auto: skip all confirmations
6. If --dry-run: print what would happen, exit

## State File

`~/.openclaw/agents/researcher/research/flywheel/state.json`:

```json
{
  "candidates": [
    {
      "topic": "edge AI for snow sports",
      "source": "conv:27/msg:8251",
      "priority": "high",
      "score": 8.5,
      "status": "pending",
      "tags": ["snowboard", "edge-ai"],
      "discoveredAt": "2026-03-18T10:00:00+10:00"
    }
  ],
  "completed": [
    {
      "topic": "snowboard apps competitive analysis",
      "reportPath": "research/reports/snowboard-apps-competitive-v3.md",
      "completedAt": "2026-03-18T14:06:00+10:00",
      "vaultNote": "snowboard-apps-competitive",
      "digest": "ShredApp leads with AI coaching, Snocru has best social features..."
    }
  ],
  "config": {
    "defaultBreadth": 4,
    "defaultDepth": 2,
    "topN": 3,
    "notifyChannel": "1483345530363973768",
    "dryRun": false
  }
}
```

## Integration Points

- **deep-research skill** — `research` delegates to `~/.openclaw/skills/deep-research/scripts/deep-research.mjs`
- **LCM tools** — `mine` uses `lcm_grep` + `lcm_describe` for conversation mining
- **Obsidian vault** — `digest` and `research` can write notes via `obsidian-cli`
- **Discord** — digest delivery via `message` tool
- **OpenClaw cron** — schedulable for automated daily runs (mine + digest cycle)

## Cron Integration

```bash
# Daily flywheel: mine topics overnight, research top 1, post morning digest
# 0 7 * * * research-flywheel run --auto --top 1
```

## Edge Cases

- **No LCM data:** If `lcm_grep` returns nothing, report "No conversation data available for mining" and exit cleanly
- **Empty state file:** Initialize with defaults on first run
- **Duplicate topics:** Dedupe by normalized topic string (lowercase, stripped)
- **Researcher agent:** Primary runner is the Researcher agent, but any agent can dispatch via `sessions_send`

## Example Session

```
User: "flywheel status"
→ Shows 2 completed, 5 pending candidates

User: "flywheel run --dry-run"
→ Would mine 3 new candidates, research "GLM-5 turbo benchmarks", post digest

User: "flywheel run"
→ Mines, asks "Research top 3 topics?", confirms, runs deep-research on each, posts digest
```