# Deep Research

Multi-round research with explicit methodology, evidence tracking, and citation-tagged...

- Repository: https://github.com/TokenRhythm/opensquilla (7104 stars)
- Category: AI and agents
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add TokenRhythm/opensquilla --skill deep-research -g -a claude-code -y`
Codex: `npx skills add TokenRhythm/opensquilla --skill deep-research -g -a codex -y`
Cursor: `npx skills add TokenRhythm/opensquilla --skill deep-research -g -a cursor -y`
Gemini CLI: `npx skills add TokenRhythm/opensquilla --skill deep-research -g -a gemini-cli -y`

## Description

Multi-round research with explicit methodology, evidence tracking, and citation-tagged synthesis. Trigger on 'deep dive', 'research report', 'literature review', 'investigate X across sources', 'multi-round investigation'. Distinct from the `summarize` skill, which is a single-pass condensation; this skill maintains a state file across iterations, tracks coverage, and produces a long-form report with per-claim citations. Three execution stages: plan (scope into sub-questions), iterate (record evidence per round), compile (synthesize report). The skill itself does not fetch the web, it tells the host agent which fetches to perform via OpenSquilla's existing web tools, and records what comes back.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# deep-research

Investigate a question by walking it through three explicit stages with a
persisted state file. Use this when a single-pass `summarize` would lose too
much, or when the user asks for a "research report" / "literature review".
The host agent does the web fetching; this skill structures the work and
keeps a paper trail.

## Decide if this is the right tool

| Need | Use |
|---|---|
| One-line summary of an article | `summarize` |
| Multi-round investigation with citations | this skill |
| Quick lookup, single source | direct web search |
| Continuous monitoring of a topic | a digest/cron skill |

## Stages

```
Scope → Plan → Iterate (×N) → Compile → Deliver
```

State persists in a single JSON file you pass between stages. The file is
the contract; if you can describe the file, you can resume the research at
any point.

---

## Stage 1: Plan

```bash
python {baseDir}/scripts/plan.py \
    --question "How did Manus differentiate from competing AI agents in 2025?" \
    --depth thorough \
    --out plan.json
```

`--depth` choices:

- `overview`, 3-5 sub-questions, target 1 source per sub-question
- `thorough`, 6-10 sub-questions, target 2-3 sources per sub-question
- `exhaustive`, 12-20 sub-questions, target 5+ sources per sub-question

The plan is a `pydantic` model serialized to JSON; see
[references/methodology.md](references/methodology.md) for the schema and
the system-review approach the depth choices implement.

---

## Stage 2: Iterate

Each round: read the plan, decide which sub-questions need attention,
print the fetch list for the host agent to execute, and (after the agent
returns results) record evidence back into the plan.

```bash
# Show the host what to fetch this round
python {baseDir}/scripts/iterate.py --plan plan.json --round 1 --print-fetches

# After the host fetches, record results back
python {baseDir}/scripts/iterate.py --plan plan.json --round 1 \
    --record evidence_round_1.json
```

`evidence_round_1.json`:

The rest: https://github.com/TokenRhythm/opensquilla/blob/HEAD/src/opensquilla/skills/bundled/deep-research/SKILL.md