# Deep Research
> Conducts deep, multi-step research on any topic by decomposing queries into a parallel execution plan, accumulating structured evidence with confidence levels, and saving output as a browsable dashboard.html in a timestamped directory. Use when the user asks to research, investigate, analyze, compare, or deep-dive a topic - especially business ideas, markets, competitors, technologies, or complex questions that require synthesizing multiple sources.
- Skill: `theadamlabs/deep-research` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add theadamlabs/deep-research`
- Raw SKILL.md: https://api.skillmd.com/api/skills/theadamlabs/deep-research/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Web & Frontend
- Author: TheAdamLabs (https://skillmd.com/u/theadamlabs)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/theadamlabs/deep-research
---
# Deep Research
Produces a **single `dashboard.html`** per run, saved to a timestamped directory. Chat output is always a short summary block pointing to it.
---
## Setup (before anything else)
1. Determine the output directory:
- If inside a project: `[project root]/research/YYYY-MM-DD-HH-MM-[query-slug]/`
- Otherwise: `~/research/YYYY-MM-DD-HH-MM-[query-slug]/`
- `query-slug` = first 4-5 words of query, lowercased, hyphenated
2. Create the directory with `mkdir -p`
3. State the full output path before proceeding
Deliverables are decided in Phase 1 based on query complexity and type. The only constant: keep chat output to a short summary block. Never paste full research into chat.
---
## Phase 1: Query Analysis
Classify the query. Write this analysis in chat (briefly) before proceeding.
**Type:**
- `comparative`: X vs Y vs Z
- `exploratory`: state of X, how X works
- `temporal`: history/evolution of X
- `decision`: should we do X
- `factual`: specific facts, numbers
- `synthesis`: best approaches to X
**Answer shape:** what does a correct answer look like as a data structure? (table / timeline / ranked list / brief+evidence / narrative)
**Failure mode:** what would make this answer wrong or useless? (outdated sources / biased sources / missing key dimension)
**Answer format:** what is the most natural structure for the answer? (narrative, ranked list, recommendation+evidence, timeline, comparison)
**Deliverables:** decide here what files to produce, based on query complexity:
| Complexity | Typical deliverables |
|---|---|
| Simple factual / quick lookup | `dashboard.html` only (minimal: summary + findings sections) |
| Moderate | `evidence.md` (working file) + `dashboard.html` |
| Complex / multi-dimensional | `evidence.md` (working file) + `dashboard.html` with full charts |
`evidence.md` is an intermediate pipeline file (subagents write to it; you read it to populate the dashboard). It is not a user deliverable. `dashboard.html` is the single user-facing output for every run.
`dashboard.html` always earns its cost: it is the only output file users read. For simple factual queries, generate a minimal dashboard (summary + findings only, skip charts and confidence table).
State the chosen deliverables here. This is the plan. Do not add files not listed.
---
## Phase 2: Research Plan (DAG)
Design as a dependency graph. Write to chat (briefly) before launching subagents.
```
INDEPENDENT (run in parallel):
[A] Sub-question A
[B] Sub-question B
[C] Sub-question C
DEPENDS ON [A]:
[D] Follow-up requiring A
DEPENDS ON [B] + [C]:
[E] Synthesis question
```
Target **6-10 sub-questions**. Each must cover ground no other sub-question covers.
---
## Phase 2b: Plan Critique (Pre-Mortem)
Before executing, run a skeptic pass. Add any blind spots as new sub-questions.
- **Anchoring**: Am I only planning to confirm my initial framing? What's the opposite framing?
- **Missing voices**: Who disagrees with the mainstream view? Is a contrarian search planned?
- **Source homogeneity**: Will all planned queries return the same 5 SEO articles?
- **Scope drift**: Am I answering a slightly different (easier) question than asked?
- **Load-bearing assumption**: What does the entire plan rest on that hasn't been verified?
Adjust the plan if needed before proceeding.
---
## Phase 3: Subagent Launch
Each independent sub-question from the DAG gets its own subagent running in parallel. Do not run searches yourself in this phase. Spawn subagents and let them do the work.
### 3a: Launch independent sub-questions
In a **single message**, launch one subagent per independent sub-question (use whatever subagent primitive your harness provides: Task tool in Cursor, `claude -p` via bash in Claude Code). Each subagent:
- Gets its own isolated context window
- Runs its own searches (minimum 5 web searches + 2 full-page fetches per subagent)
- Writes results to `evidence-[ID].md` in the shared output directory
- Returns a one-paragraph summary of what it found
**Subagent prompt template** (fill in the bracketed parts):
```
You are a research agent. Your job is to answer one specific sub-question as part of a larger research task.
ORIGINAL QUERY: [full original user query]
YOUR SUB-QUESTION: [sub-question text]
OUTPUT FILE: [full path to evidence-[ID].md]
OUTPUT DIRECTORY: [full output directory path]
Instructions:
1. Run at least 5 web searches and fetch 2 full pages to research your sub-question thoroughly.
Vary your query angles:
- Broad: "[topic] 2026"
- Specific: "[topic] [metric] site:[authority-domain]"
- Contrarian: "[topic] problems failure criticism"
- Comparative: "[topic] vs [alternative]"
2. Extract atomic claims (not summaries). Each claim is one sentence.
3. Write ALL findings to [full path to evidence-[ID].md] using this format:
## [Sub-question text]
CLAIM: [one-sentence fact]
SOURCE: [URL]
FRESHNESS: [date or "unknown"]
CONFIDENCE: high | medium | low | conflicted
NOTE: [only if conflicted]
CLAIM: ...
4. After writing the file, return a short paragraph summarizing:
- The 2-3 most important findings
- The overall confidence level
- Any significant gaps or conflicts you found
```
### 3b: Launch dependent sub-questions
After independent subagents complete, read their `evidence-[ID].md` files, then launch subagents for dependent sub-questions, passing relevant findings from dependencies as context in the prompt.
Use the same subagent prompt template, adding:
```
CONTEXT FROM PRIOR RESEARCH:
[paste relevant claims from dependency evidence files]
```
### 3c: Merge evidence
After all subagents complete, merge all `evidence-[ID].md` files into `evidence.md`:
```bash
cat [output-dir]/evidence-*.md > [output-dir]/evidence.md
```
**Minimum search budget across all subagents combined: 25 web searches + 10 full-page fetches.** If the total falls short (check by counting searches in evidence files), run additional searches yourself to fill the gap before moving to Phase 4.
---
## Phase 4: Evidence Store
Append to `evidence.md` after each search batch:
```
## [Sub-question ID]: [Sub-question text]
CLAIM: [one-sentence atomic fact]
SOURCE: [full URL; never a domain name or title alone]
FRESHNESS: [date or "unknown"]
CONFIDENCE: high | medium | low | conflicted
NOTE: [only if conflicted - what each source says]
CLAIM: ...
```
**SOURCE field rules - no exceptions:**
- Always the full URL (`https://...`). Never a bare domain, publication name, or "source unavailable".
- If a claim comes from a paywalled or unresolvable page, record the URL anyway and mark FRESHNESS "paywalled".
- One SOURCE per CLAIM. If two sources back the same claim, record the claim twice with different SOURCE lines.
**Confidence rules:**
- `high`: 2+ independent primary sources agree
- `medium`: 1 credible primary source, or multiple secondary sources
- `low`: single secondary source, or inference
- `conflicted`: sources disagree; document both positions
---
## Phase 5: Gap Analysis
After merging all evidence, run gap check against `evidence.md`:
- Sub-questions with only `low`/`conflicted` evidence: spawn a targeted subagent with a tighter, more specific prompt
- New sub-questions that emerged during research: add to plan and spawn a subagent
- Load-bearing claims with weak sourcing: spawn a subagent specifically tasked with finding a primary source or disconfirming evidence
- Same sources appearing repeatedly: stop, that search space is saturated
For each gap requiring more research, spawn one subagent per gap (in parallel if multiple gaps). Merge results back into `evidence.md`. One gap-fill round maximum.
---
## Phase 6: Synthesis
Hold in context -- do not write to a file yet.
Synthesize what you found into a clear, direct answer. Write it as you naturally would: prose narrative, structured lists, a recommendation with evidence, whatever form best communicates the findings. Do not force a format.
Every factual claim must end with an inline source link `[[domain.com]](https://full-url)`. Note where sources conflict. Note what you could not find.
For the confidence donut: count `CONFIDENCE: high/medium/low/conflicted` lines in `evidence.md` -- you will need these totals for `data-high/med/low/conf` on `#confidence-donut` in Phase 8.
---
## Phase 7: Output Evaluation + Patch Loop
Score your Phase 6 synthesis (held in context) against the original query:
```
COMPLETENESS [0-3] covers all meaningful dimensions?
ACCURACY [0-3] load-bearing claims have high-confidence sources?
RELEVANCE [0-3] answers the actual question, not an easier adjacent one?
ARTIFACT FIT [0-3] right format for the query type?
TOTAL: /12
```
- **10-12**: proceed to Phase 8 (dashboard)
- **7-9**: patch: spawn one subagent targeting the lowest-scoring dimension, update the relevant section of your synthesis in context, re-score once
- **0-6**: restart Phase 1 (framing was wrong, patching won't fix it)
Max 2 patch rounds. After 2, deliver with explicit notes on what remains weak.
Patch subagent prompt adds:
```
PATCH CONTEXT: This is a targeted patch for a deep research run.
WEAK DIMENSION: [completeness / accuracy / relevance / artifact fit]
GAP: [specific description of what's missing]
EXISTING FINDINGS: [paste relevant section of evidence.md]
YOUR TASK: Find sources that specifically address this gap. Return findings in evidence format.
```
---
## Phase 8: Dashboard
Write `[output-dir]/dashboard.html` from scratch in a single `Write` call.
**Stack: DaisyUI 5 + Tailwind CSS 4 via CDN** (file requires internet to open).
All component patterns you need are in the section guide below. If you need more DaisyUI detail, check https://daisyui.com/components/.
### Page skeleton
```html
[Query title]: Deep Research
Deep Research
[Query title]
[Date] · [N] sources · [query type]
```
### Section guide
**Page header** (before all sections) -- include score as a compact inline note so it stays visible without dominating:
```html
[Query title]
[Date] · [N] sources · Score: [X]/12
```
**`#summary`** -- research output only, no quality metadata here.
1. Headline: `
` (1-2 sentence direct answer to the query)
2. Key insights: 4-6 `