Best use case
Research Skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
## Description
Teams using Research Skill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/parallel-ai-research/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Research Skill Compares
| Feature / Agent | Research Skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
## Description
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Research Skill
## Description
Conduct open-ended research on a topic, building a living markdown document. The conversation is ephemeral; the document is what matters.
## Trigger
Activate when the user wants to:
- Research a topic, idea, or question
- Explore something before committing to building it
- Investigate options, patterns, or approaches
- Create a "research doc" or "investigation"
- Run deep async research on a complex topic
## Research Directory
Each research topic gets its own folder:
```
~/.openclaw/workspace/research/<topic-slug>/
├── prompt.md # Original research question/prompt
├── research.md # Main findings (Parallel output or interactive notes)
├── research.pdf # PDF export (when generated)
└── ... # Any other related files (data, images, etc.)
```
---
## Two Research Modes
### 1. Interactive Research (default)
For topics you explore together in conversation. You search, synthesize, and update the doc in real-time.
### 2. Deep Research (async)
For complex topics that need comprehensive investigation. Uses the Parallel AI API via `parallel-research` CLI. Takes minutes to hours, returns detailed markdown reports.
**When to use deep research:**
- Market analysis, competitive landscape
- Technical deep-dives requiring extensive source gathering
- Multi-faceted questions that benefit from parallel exploration
- When user says "deep research" or wants comprehensive coverage
---
## Interactive Research Workflow
### 1. Initialize Research
1. **Create the research folder** at `~/.openclaw/workspace/research/<topic-slug>/`
2. **Create prompt.md** with the original question:
```markdown
# <Topic Title>
> <The core question or curiosity>
**Started:** <date>
```
3. **Create research.md** with the working structure:
```markdown
# <Topic Title>
**Status:** Active Research
**Started:** <date>
**Last Updated:** <date>
---
## Open Questions
- <initial questions to explore>
## Findings
<!-- Populated as we research -->
## Options / Approaches
<!-- If comparing solutions -->
## Resources
<!-- Links, references, sources -->
## Next Steps
<!-- What to explore next, or "graduate to project" -->
```
4. **Confirm with user** - Show the folder was created and ask what to explore first.
### 2. Research Loop
For each exchange:
1. **Do the research** - Web search, fetch docs, explore code
2. **Update the document** - Add findings, move answered questions, add sources
3. **Show progress** - Note what was added (don't repeat everything)
4. **Prompt next direction** - End with a question or suggestion
**Key behaviors:**
- Update existing sections over creating new ones
- Use bullet points for findings; prose for summaries
- Note uncertainty ("seems like", "according to X", "unverified")
- Link to sources whenever possible
### 3. Synthesis Checkpoints
Every 5-10 exchanges, offer to:
- Write a "Current Understanding" summary
- Prune redundant findings
- Reorganize if unwieldy
- Check blind spots
### 4. Completion
When research is complete, update the status in `research.md`:
- **"Status: Complete"** — Done, stays in place as reference
- **"Status: Ongoing"** — Living doc, will be updated over time
**If the research is specifically for building a project:**
- Graduate to `~/specs/<project-name>.md` as a project spec
- Or create a project directly based on findings
- Update status to **"Status: Graduated → ~/specs/..."**
Most research is just research — it doesn't need to become a spec. Only graduate if you're actually building something from it.
---
## Deep Research Workflow
### 1. Start Deep Research
```bash
parallel-research create "Your research question" --processor ultra --wait
```
**Processor options:**
- `lite`, `base`, `core`, `pro`, `ultra` (default), `ultra2x`, `ultra4x`, `ultra8x`
- Add `-fast` suffix for speed over depth: `ultra-fast`, `pro-fast`, etc.
**Options:**
- `-w, --wait` — Wait for completion and show result
- `-p, --processor` — Choose processor tier
- `-j, --json` — Raw JSON output
### 2. Schedule Auto-Check (OpenClaw)
After creating a task, set up a cron job to check results and deliver them back to the user. Use `deleteAfterRun: true` so it cleans up automatically.
**⚠️ CRITICAL: Always calculate `atMs` correctly!**
```bash
# Get current timestamp in ms and add 15 minutes (900000 ms)
date +%s%3N # Current time in epoch ms
# Example: 1770087600000 + 900000 = 1770088500000
```
**Always verify the scheduled time is in the future and has the correct year:**
```bash
date -d @$((1770088500000/1000)) # Should show a time ~15 min from now, correct year
```
```json
{
"action": "add",
"job": {
"name": "Check research: <topic>",
"schedule": {"kind": "at", "atMs": <VERIFY: must be current epoch ms + delay>},
"sessionTarget": "isolated",
"payload": {
"kind": "agentTurn",
"message": "Check research task <run_id>. Run: parallel-research result <run_id>. If complete, summarize key findings. If still running, reschedule another check in 10 min.",
"deliver": true,
"channel": "<source channel, e.g. telegram>",
"to": "<source chat/topic, e.g. -1001234567890:topic:123>"
},
"deleteAfterRun": true
}
}
```
**Key points:**
- Use the `cron` tool with `action: "add"`
- **ALWAYS verify `atMs` is correct** — run `date -d @$((atMs/1000))` to confirm year and time
- `atMs` should be ~10-15 min from now (ultra processor) or ~5 min (fast processors)
- `deleteAfterRun: true` removes the job after successful completion
- Deliver back to the same channel/topic that requested the research
- If still running, the cron job can create another check
- `PARALLEL_API_KEY` is available as env var — no need to inline it
### 3. Manual Check (if needed)
```bash
parallel-research status <run_id>
parallel-research result <run_id>
```
### 4. Save to Research Folder
Create the research folder and save results:
```
~/.openclaw/workspace/research/<topic-slug>/
├── prompt.md # Original question + run metadata
├── research.md # Full Parallel output
```
**prompt.md** should include:
```markdown
# <Topic Title>
> <Original research question>
**Run ID:** <run_id>
**Processor:** <processor>
**Started:** <date>
**Completed:** <date>
```
**research.md** contains the full Parallel output, plus any follow-up notes.
---
## PDF Export
Use the `export-pdf` script to convert research docs to PDF:
```bash
export-pdf ~/.openclaw/workspace/research/<topic-slug>/research.md
# Creates: ~/.openclaw/workspace/research/<topic-slug>/research.pdf
```
Or specify a custom output path:
```bash
export-pdf research.md ~/Desktop/output.pdf
```
**Note:** Tables render as stacked rows (PyMuPDF limitation). Acceptable for research docs.
---
## Commands
- **"new research: <topic>"** - Start interactive research doc
- **"deep research: <topic>"** - Start async deep research
- **"show doc"** / **"show research"** - Display current research file
- **"summarize"** - Synthesis checkpoint
- **"graduate"** - Move research to next phase
- **"archive"** - Mark as complete reference
- **"export pdf"** - Export to PDF
- **"check research"** - Check status of pending deep research tasks
---
## Document Principles
- **Atomic findings** - One insight per bullet
- **Link everything** - Sources, docs, repos
- **Capture context** - Why did we look at this?
- **Note confidence** - Use qualifiers when uncertain
- **Date important findings** - Especially for fast-moving topics
---
## Setup
See `SETUP.md` for first-time installation of:
- `parallel-research` CLI
- PDF export tools (pandoc, PyMuPDF)Related Skills
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