deep-research

name	deep-research
description	Routing front door for a structured, human-in-the-loop deep-research workflow (Weizhena/Deep-Research-skills) — turn a topic into an extensible outline, fan out parallel web-search agents to investigate each item into validated JSON, then render a complete markdown report. One skill, 4 reference pipelines: outline (research / add-items / add-fields), deep (parallel per-item investigation + field-coverage validation), report (TOC + per-field markdown), web-search (research agent + 5 routed source modules). Plugin: npx skills add https://github.com/akillness/jeo-skills --skill deep-research
allowed-tools	Read Write Edit Bash Grep Glob WebSearch WebFetch Task
compatibility	Universal — every pipeline is plain Markdown instructions. The deep and report phases run two small Python helpers (validate_json.py needs PyYAML; the report generator is emitted per-run), so those phases need Python 3 + pyyaml. Web search uses each agent's native WebSearch (OpenCode requires OPENCODE_ENABLE_EXA=1). Works in Claude Code, Codex, Gemini CLI, OpenCode, Cursor, Copilot. Plugin-installable via `npx skills add`.
metadata	{"tags":"deep-research, structured-research, research-outline, web-search, literature-review, benchmark, competitor-analysis, due-diligence, parallel-agents, report-generation, human-in-the-loop","platforms":"Claude, Codex, Gemini, Cursor, Copilot, OpenCode, All","keyword":"deep-research","version":"1.0.3","upstream":"https://github.com/Weizhena/Deep-Research-skills","source":"akillness/jeo-skills","license":"MIT"}

deep-research

A routing-first front door for the Deep Research workflow from Weizhena/Deep-Research-skills — a two-phase, human-in-the-loop research method (outline generation, then deep investigation) consolidated into one jeo-skill with 4 reference pipelines. Each pipeline ships its own prompt templates and output contract; this skill classifies the request into the right phase, loads that pipeline, and executes it exactly.

One topic → one extensible outline → parallel per-item investigation into validated JSON → one complete markdown report. Every phase has a user checkpoint, so you keep precise control at each stage instead of handing the model a black-box "research X" prompt.

The reference pipelines hold the dismantled-and-merged upstream skill text (per-command frontmatter stripped, headings nested, prompt templates kept verbatim) — not a paraphrase — so each phase is self-contained here. Read the matching pipeline reference before executing:

• references/outline-pipeline.md — /research · /research-add-items · /research-add-fields (Phase 1: generate + extend the outline)
• references/deep-pipeline.md — /research-deep (Phase 2: parallel per-item investigation + validate_json.py coverage gate)
• references/report-pipeline.md — /research-report (Phase 3: TOC + per-field markdown report)
• references/web-search-pipeline.md — the web-search agent + 5 routed source modules (github-debug · general-web · academic-papers · chinese-tech · stackoverflow)

Plugin Installation

# This routing skill via jeo-skills (verified path)
npx skills add https://github.com/akillness/jeo-skills --skill deep-research

# Global install for one or more agents
npx skills add -g https://github.com/akillness/jeo-skills --skill deep-research -a claude-code -a codex -y

# Scripted install with knobs (Python dep + upstream slash-command skills)
WITH_DEPS=1 AGENTS="claude-code,codex" bash .agent-skills/deep-research/scripts/install.sh

The deep phase calls scripts/validate_json.py (needs pip install pyyaml). The upstream repo also ships ready-made slash commands for Claude Code, OpenCode, and Codex — clones and copies them.