deepresearch-search
Stage 2b of the lit review pipeline: run a Google Gemini Deep Research deep search (Interactions API) from the brief produced by Stage 0, then parse the cited report into the pipeline schema. API-driven (GEMINI_API_KEY), no browser. An alternative deep-search pathway alongside Undermind (Stage 1) and Scholar Labs (Stage 2). Only use this skill when explicitly requested. Do NOT auto-trigger on general literature review or paper search requests.
npx skills add kennethkhoocy/applied-micro-skills --skill deepresearch-search --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 Search (Stage 2b) Takes the Stage-0 brief and uses the **Gemini Deep Research Agent** to autonomously plan, search, read, and synthesize the prior literature, then parses the resulting cited report into `<stem>.json` + `<stem>.bib` for dedup and screening. It complements the other deep searches: Undermind and Scholar Labs are browser-driven; this one is a pure API call. The stage is two halves: - **`scripts/deepresearch_search.py`** — calls the Interactions API (`deep-research-max-preview-04-2026` by default), runs the task in the background, polls to completion, and saves the report + raw response. - **`scripts/deepresearch_ingest.py`** — UI-independent parsing. It reads the agent's `KEY PAPERS` section (and, as a fallback, citations in the raw response), best-effort-enriches via Crossref, and writes the pipeline JSON. Importable, and runnable standalone on a saved report. ## The flow `brief` → wrapped into a literature-review prompt that ends with a parseable `KEY PAPERS` section (`Title | Authors | Year | Venue | DOI or URL`, one per line) → `POST /v1beta/interactions` with `background=true`, `store=true`, agent `deep-research-max-preview-04-2026` → poll `GET /v1
- The flow
- Why an API, not a browser
- API key
- Cost
- CLI
- Graceful degradation
- Source extraction
- Output schema
- Notes / limitations
Driven by the orchestrator (the normal path) python scripts/deepresearch_search.py --query-file undermind_brief.txt \ Re-parse a saved report (no API call) python scripts/deepresearch_ingest.py --report debug_deepresearch/deepresearch_report.md \
What does the deepresearch-search skill do?
Stage 2b of the lit review pipeline: run a Google Gemini Deep Research deep search (Interactions API) from the brief produced by Stage 0, then parse the cited report into the pipeline schema. API-driven (GEMINI_API_KEY), no browser. An alternative deep-search pathway alongside Undermind (Stage 1) and Scholar Labs (Stage 2). Only use this skill when explicitly requested. Do NOT auto-trigger on general literature review or paper search requests.
How do I install it?
Run `npx skills add kennethkhoocy/applied-micro-skills --skill deepresearch-search --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 kennethkhoocy/applied-micro-skills, a repository with 54 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.