pi-deep-research

Deep research skill for pi — structured search, reflection, and analysis.

Instead of shallow search-and-summarize, it enforces structured methodology: plan → search → reflect → iterate → report. A code-enforced checkpoint gate prevents the agent from rushing to conclusions before gathering enough evidence.

Install

pi install npm:pi-deep-research

Then set a search API key (at least one):

# Tavily (recommended, free: 1000 req/month)
export TAVILY_API_KEY="tvly-..."

# Brave Search (alternative, free: 2000 req/month)
export BRAVE_API_KEY="BSA..."

Usage

Slash Command

/research [depth] [topic]

Depth levels:

Depth	Searches	Sources	Confidence	Time
quick	1-3	3-5	60%	~2 min
standard	3-6	5-10	75%	~5 min
deep	5-10	10-15	85%	~10 min
exhaustive	10-20	15-30	95%	~20 min

Examples:

/research quick what is MCP protocol
/research deep competitive analysis of AI coding assistants
/research exhaustive quantum computing applications in drug discovery

Natural Language

The skill also activates when you ask the agent to research, investigate, or survey a topic:

Investigate the current state of AI agent frameworks
Investigate the current state of WebAssembly adoption

Quick Start

/research deep AI agent frameworks comparison 2026

Produces a comprehensive Markdown research report with executive summary, cross-referenced analysis, source credibility ratings, and contradiction tracking.

Demo

1. Start a research task

2. Review and approve the plan

The agent presents sub-questions and search queries, then waits for your approval before spending API calls.

3. Checkpoint: keep searching or proceed?

After each search round, the research_checkpoint tool evaluates progress. Here it says 🔴 CONTINUE — confidence is too low and contradictions need resolving:

After more rounds, all criteria are met — 🟢 PROCEED:

4. Research complete

The agent generates a structured Markdown report with findings summary:

5. Full report output

A comprehensive research report with Executive Summary, Key Findings, cross-referenced analysis, and source citations:

💡 Tips: Visual HTML report

Pair with visual-explainer to turn the Markdown report into a styled HTML page:

pi install https://github.com/nicobailon/visual-explainer

Then ask: Turn this report into a visual HTML page

Why

LLMs doing "research" typically search once, skim snippets, and produce a surface-level summary. This skill fixes that by:

1. Forcing deep reading — instructs the agent to use web_extract on substantive sources, not just rely on search snippets
2. Code-enforced reflection — a research_checkpoint tool that evaluates progress against hard thresholds (min rounds, min sources, confidence score) and returns CONTINUE/PROCEED verdicts the agent must obey
3. Multi-hop reasoning — Entity Expansion, Temporal Progression, Conceptual Deepening, and Causal Chain patterns with concrete examples
4. Analytical writing — anti-patterns ("Source A says X. Source B says Y." ❌) vs analytical style ("Evidence converges on X because..." ✅)
5. Human-in-the-Loop — research plan must be approved before execution begins

How It Works

4-Phase Workflow

Phase 1: Understand & Plan
  ↓ (user approves plan)
Phase 2: Search & Gather (multi-hop reasoning, deep reading)
  ↓
Phase 3: Checkpoint & Reflect (MANDATORY — code-enforced)
  ↓ 🔴 CONTINUE? → back to Phase 2
  ↓ 🟢 PROCEED? → continue
Phase 4: Synthesize & Report (Markdown file)

Research Checkpoint (the key innovation)

After every search round, the agent must call the research_checkpoint tool. This tool runs 6 hard rules:

Rule	What it checks
Min search rounds	Haven't done enough rounds for this depth level
Min sources	Not enough unique sources collected
Answered ratio	Too many sub-questions still unanswered
Avg confidence	Overall confidence below depth threshold
Low-confidence questions	Any sub-question below 40% confidence
Unresolved contradictions	Sources disagree and it hasn't been resolved

If any rule fails → 🔴 CONTINUE (with specific guidance on what to search next). All rules pass → 🟢 PROCEED (agent may write the report).

Safety valve: after max rounds, forces PROCEED and flags remaining gaps.

Multi-Hop Reasoning Patterns

• Entity Expansion: Product → Company → Competitors → Market position
• Temporal Progression: Current state → Recent changes → Historical context
• Conceptual Deepening: Overview → Architecture → Trade-offs → Edge cases
• Causal Chain: Observation → Immediate cause → Root cause → Solutions
• Source Triangulation: Official docs × Independent analysis × Community experience

Report Output

Reports are saved as Markdown files: research_[topic]_[YYYYMMDD].md

Sections include:

• Executive Summary — conclusion first, then evidence
• Key Findings — ranked by importance with source citations
• Detailed Analysis — cross-referenced sub-questions with original analysis
• Comparison Table + Narrative — data and insight together
• Contradictions & Debates — vendor claims vs independent evidence
• Uncertainties & Gaps — explicitly flagged low-confidence areas
• Recommendations — primary, alternative, not recommended
• Sources Table — every URL with date and credibility tier (⭐🔵🟡🔴)

Package Contents

File	Purpose
SKILL.md	Research workflow, behavioral mindset, multi-hop patterns, checkpoint rules
extension.ts	web_search + web_extract + research_checkpoint tools
prompts/research.md	/research slash command template
references/config.md	Depth thresholds, credibility tiers, confidence formula
references/report-template.md	Report structure, writing anti-patterns, quality requirements

Configuration

Search Providers

Provider	Env Variable	Free Tier
Tavily (recommended)	TAVILY_API_KEY	1000 req/month
Brave Search	BRAVE_API_KEY	2000 req/month

The extension tries Tavily first, falls back to Brave. If neither is set, it shows a helpful error.

Depth Defaults

Override in references/config.md:

• Confidence thresholds per depth level
• Min/max search rounds
• Source count requirements
• Credibility tier weights

Design Decisions

This skill is built on insights from SuperClaude's DeepResearch architecture and academic foundations including:

• Reflexion (Shinn et al. 2023) — self-reflective loops with explicit evaluation
• Chain-of-Thought (Wei et al. 2022) — structured reasoning decomposition
• ReAct (Yao et al. 2023) — interleaved reasoning and action
• Multi-hop QA (Yang et al. 2018) — cross-document reasoning

Key design principles:

1. Forceful imperative wording for reference file loading — LLMs skip polite requests
2. Exact keyword matching for depth selection — prevents natural-language ambiguity from overriding explicit depth choices
3. Human-in-the-Loop at plan stage — API calls are costly, confirm before executing
4. Code-enforced checkpoints — LLMs self-evaluate optimistically, code doesn't

License

MIT