deep-research
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 t
Profile →npx skills add opensquilla/opensquilla --skill deep-research --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Decide if this is the right tool
- Stages
- Stage 1: Plan
- Stage 2: Iterate
- Stage 3: Compile
- Boundaries
- Differentiation from summarize
python {baseDir}/scripts/plan.py \
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 \
python {baseDir}/scripts/compile.py --plan plan.json --out report.mdWhat does the deep-research skill do?
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 t
How do I install it?
Run `npx skills add opensquilla/opensquilla --skill deep-research --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From opensquilla/opensquilla, a repository with 6,515 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.