Deep Research
Multi-round research with explicit methodology, evidence tracking, and citation-tagged...
Install
npx skills add TokenRhythm/opensquilla --skill deep-research -g -a claude-code -y- Repository
- TokenRhythm/opensquilla7.1k
- Category
- AI and agents
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Lightabout 1,032 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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
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-questionthorough, 6-10 sub-questions, target 2-3 sources per sub-questionexhaustive, 12-20 sub-questions, target 5+ sources per sub-question
The plan is a pydantic model serialized to JSON; see
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.
# 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:
