# Dishantpal Deep Research Skill Deep Research Skill > Deep Research Skill - Skill: `tomevault-io/dishantpal-deep-research-skill-deep-research-skill` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/dishantpal-deep-research-skill-deep-research-skill` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/dishantpal-deep-research-skill-deep-research-skill/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/dishantpal-deep-research-skill-deep-research-skill --- # Deep Research Skill ## Core Philosophy Most research is shallow. It covers what's easily Googlable, presents generic overviews, and stops at the surface. This skill exists to go far beyond that. **The Delivery Rule:** - Never deliver less than 7x of what was asked - Aim for 10-15x: thorough coverage plus adjacent territory the user didn't request - For anything in the 25x zone (interesting but outside scope), include brief pointers so nothing important stays invisible **The Quality Standard:** Every section must pass the "experienced practitioner" test — would someone who's been in this space for 5 years learn something new from this output? If not, go deeper. Surface-level overviews are never acceptable as a final deliverable. --- ## Step 1: Mode Detection Determine the research mode before doing anything else. ### Mode A: Guided (Interactive) **Trigger when:** The prompt is vague, broad, ambiguous, or could go in multiple very different directions. Or the user explicitly says "ask me questions first." **Process:** 1. Ask 3-5 sharp questions using the Question Decomposition technique (below) 2. Present a Research Plan and confirm 3. Execute **Question Decomposition Technique:** Do not ask generic questions. Decompose the research space into decision-relevant dimensions: - **Goal Question:** "What decision will this research inform?" (Not "what do you want to know" — that's circular) - **Boundary Question:** "What's explicitly out of scope?" (Prevents going wide in the wrong direction) - **Knowledge Question:** "What do you already know or believe about this?" (Avoids repeating known info, surfaces assumptions to test) - **Constraint Question:** "Are there non-negotiable constraints?" (Budget, timeline, regulatory, technical) - **Success Question:** "If this research goes perfectly, what does it let you do tomorrow?" (Reveals the real goal behind the stated goal) Pick 3-5 of these based on what's actually ambiguous. Never ask all 5 if 3 will do. ### Mode B: Autonomous (Self-Contained) **Trigger when:** The prompt is specific, detailed, and provides clear context. Or the user says "just go" or gives a complete brief. **Process:** 1. Internally build the Research Plan 2. Execute immediately 3. Deliver the full output ### Ambiguous Cases Default to 2 quick questions max. The user would rather get 80% right research fast than wait through 10 questions for 95% right. --- ## Step 2: Research Planning ### Build the Internal Research Plan Every research execution builds this plan (shown to user in Guided mode, internal in Autonomous): 1. **Core Question** — One sentence stating exactly what we're trying to understand 2. **Research Category** — Classify the topic (see Playbooks in `references/playbooks.md`) 3. **Framework Selection** — Which analytical frameworks apply? (see `references/frameworks.md`) 4. **Layer Map** — What belongs in each of the 5 layers for this specific topic? 5. **Source Strategy** — Which sources carry the most weight? (see `references/source-selection.md`) 6. **Known Unknowns** — What do we know we don't know? 7. **Hypothesized Unknown Unknowns** — What might exist that hasn't been thought to look for? ### Framework Selection Logic Before executing research, select 1-3 analytical frameworks from `references/frameworks.md` that fit the topic. Framework selection follows this logic: | If the research involves... | Use these frameworks | |---|---| | Entering a new market or industry | PESTEL + Porter's Five Forces + Market Segmentation | | Understanding customer needs or product-market fit | Jobs-to-be-Done + Value Proposition Canvas | | Evaluating competitive position | Competitive Positioning Map + Porter's Five Forces | | Assessing a business opportunity | Business Model Canvas + SWOT (done properly) | | Regulatory or policy landscape | Regulatory Landscape Mapping + PESTEL | | Technology or product decisions | Technology Adoption Lifecycle + Build vs Buy Matrix | | Solving an operational problem | Root Cause Analysis + Analogous Industry Scan | Read `references/frameworks.md` for full framework instructions before applying them. Do not use frameworks you haven't read the instructions for — a poorly applied framework is worse than no framework. --- ## Step 3: Research Execution — The 5 Layers ### Layer 1: Surface Scan The easily accessible information. Table stakes. **Capture:** Key players, standard definitions, basic market data, conventional wisdom, the "Wikipedia version." **Time allocation:** ~10% of effort. Get this done fast and move on. **Quality gate:** Can you name the top 5-10 players, define the key terms, and state the market size (if applicable) with a source? If yes, Layer 1 is done. ### Layer 2: Structure & Landscape How the space is organized — not a list, but a map with decision logic. **Capture:** Market segments with real differentiators, business model variations with tradeoffs, regulatory framework, the decision tree of how options branch. **The key technique:** For every option, answer three questions: - When does this option WIN? (What conditions make it the best choice?) - What does this option SACRIFICE? (What do you give up?) - Who actually CHOOSES this option and why? (Real examples, not hypotheticals) **Quality gate:** Could someone use this section to make a preliminary decision about which segment/approach to pursue? If not, it's too shallow. ### Layer 3: Depth Dive (Where the Value Lives) This is the most important layer. Most research stops at Layer 2. This skill keeps going. **Capture:** - Real-world case studies (successes AND failures, with specifics — names, numbers, timelines) - Implementation gotchas and hidden costs nobody mentions upfront - Regulatory edge cases and how they're actually enforced (vs. what's written) - What experienced practitioners say privately vs. the official narrative - Second-order effects ("if you do X, then Y also happens, which causes Z") - Failure modes and the common mistakes that cause them - The gap between marketing claims and operational reality **Where to find Layer 3 information:** - Reddit and niche forums (real practitioner experiences, search for specific terms + "reddit") - Negative reviews on G2, Capterra, App Store (reveals real pain points) - Government enforcement actions and audit findings - Conference talks and podcast interviews (practitioners are more candid in conversation) - Support forums and help documentation (reveals actual problems) - Job postings (reveal where companies are investing and struggling) - Regulatory comment periods (reveal industry concerns) - Court filings and FTC actions (reveal what goes wrong) - Academic "limitations" sections (researchers are honest about gaps) **The Absence Test:** After gathering Layer 3 information, ask: "What am I NOT seeing? What should exist here but doesn't?" Absence of information is itself informative — if nobody is writing about failure cases, either the space is too new or failures are being hidden. **Quality gate:** Does this section contain at least 3 specific, non-obvious insights that would surprise someone with surface-level knowledge of the topic? If not, go deeper. ### Layer 4: Adjacent Opportunities (10-15x Zone) Things the user didn't ask about but should know exist. **Identification techniques:** - **Customer-outward:** "Who else serves this customer, and what else do they need?" - **Solution-outward:** "What other problems does this solution partially address?" - **Regulatory-outward:** "What other rules/programs share the same regulatory framework?" - **Technology-outward:** "What other applications use the same underlying technology?" - **Business-model-outward:** "What other businesses use a similar revenue/delivery model?" **For each adjacency, provide:** - What it is (1-2 sentences) - Why it's relevant to the original question - Signal strength (high / medium / low) - The one thing to search if the user wants to explore it **Quality gate:** At least 3 adjacent opportunities identified. At least 1 should be genuinely surprising. ### Layer 5: Horizon Pointers (25x Zone) Brief flags for things that exist but are outside scope. **Format:** 3-8 items, each with a one-liner, why it might matter, and a starting point for exploration. **Quality gate:** These should be things the user would thank you for mentioning, not obvious filler. --- ## Step 4: Synthesis After gathering information across all 5 layers, do NOT dump findings into the output template. First, synthesize. Read the full synthesis methodology at: `references/synthesis-engine.md` The core synthesis steps are: ### 4a. Triangulate Cross-reference findings from different source types. Where do 3+ independent sources agree? That's high-confidence. Where do sources contradict? That needs explicit treatment. ### 4b. Identify Patterns Look across all findings for: - **Convergence patterns:** Multiple unrelated sources pointing to the same conclusion - **Divergence patterns:** A clear split between two camps (both may be right in different contexts) - **Silence patterns:** Something that should be discussed but isn't - **Trend patterns:** Directional movement across time ### 4c. Extract Insights (Not Just Facts) An insight is a fact + context + implication. Transform raw findings: - **Fact:** "Company X launched feature Y in 2024" - **Insight:** "Company X launched feature Y in 2024, which signals that the market is moving toward Z. This matters because it changes the competitive dynamics for anyone entering now — you'd need to match Y as table stakes rather than treating it as a differentiator." Every major finding in the output should be elevated from fact to insight. ### 4d. Red Team Your Own Conclusions Before finalizing, deliberately attempt to disprove the emerging narrative. This is mandatory, not optional. **Red Team Checklist:** - What's the strongest argument AGAINST the conclusion forming in this research? - What would need to be true for the opposite conclusion to hold? - Is there survivorship bias? (Are we only seeing the successes?) - Is there recency bias? (Are we overweighting recent events?) - Is there source bias? (Are most of our sources from one perspective?) - What would a skeptic say about this research? - What's the most likely way this research could be WRONG? Surface the strongest counter-argument in the output. If the research conclusion still holds after red-teaming, say so and explain why. If the red team raises genuine doubts, flag them prominently. ### 4e. Build the Narrative Arc The output should tell a story, not present a filing cabinet. Before writing, identify: - What's the single most important thing the reader should understand? - What's the natural order of revelation? (What does the reader need to understand first before the next piece makes sense?) - Where are the "turns" — the moments where the obvious assumption gets challenged? --- ## Step 5: Output Construction ### Format Selection - **Markdown (.md):** Default for most research. Quick to read, easy to reference. - **Word document (.docx):** Use when the topic is formal, the output is very long (5000+ words), or the context suggests the deliverable will be shared externally. ### Document Structure ``` # [Research Topic]: Deep Research Brief ## TL;DR 3-5 sentences. The most important findings. A busy person who reads only this walks away with the key insight. This is NOT a summary of what the doc covers — it's the answer. ## Context & Scope What was researched, why, and what's explicitly in/out of scope. State the frameworks used and why. ## The Landscape Layer 1 + Layer 2 combined into a coherent narrative. Use the most appropriate structure for the topic: - Comparison tables for evaluating alternatives - Decision trees for branching choices - Market maps for competitive positioning - Segment breakdowns for market entry Apply the selected analytical framework(s) here. ## [Custom Title for the Depth Section] Layer 3 content. Title this section based on the actual content: - "Implementation Reality" for product/feature research - "What Practitioners Actually Experience" for market research - "Regulatory Deep Cuts" for compliance research - "How Others Solved This" for problem-solving research Subsections follow the natural structure of findings. Use specific case studies, contrasting perspectives, and concrete examples. ## Adjacent Opportunities Layer 4 content. Each item structured with: - What it is - Why it's relevant - Signal strength - Next step to explore ## Competing Perspectives & Counterarguments Red team output. The strongest case against the emerging conclusions. Why the research might be wrong. What would need to be true for the opposite conclusion to hold. ## Decision Points (If the research informs a decision) Crystallize key tradeoffs into clearly framed choices: - Option A: [description] → leads to [outcome], sacrifices [tradeoff] - Option B: [description] → leads to [outcome], sacrifices [tradeoff] Not recommendations unless explicitly asked — just cleanly framed decisions with consequences. ## Horizon: Worth Knowing About Layer 5 content. Brief pointers to the 25x zone. ## Sources & Confidence What sources were used, why chosen, confidence level on key claims. Flag anything where information was thin, contradictory, or limited. Rate overall confidence: HIGH / MEDIUM / LOW with reasoning. ``` ### Writing Standards - Write in prose, not bullet dumps. Bullets only for genuinely list-like content - Every substantive claim gets a source or confidence level - Competing perspectives sit side by side, not buried - Numbers always get context (market size needs growth rate, comparable benchmarks, or "so what") - Tables for comparisons (2+ options, 3+ dimensions) - Section lengths proportional to importance, not equal - No filler phrases ("in today's rapidly evolving landscape," "it's important to note") - No jargon without definition on first use - Active voice, direct statements - When uncertain, say so explicitly with reasoning --- ## Execution Checklist (Verify Before Delivering) - [ ] TL;DR could stand alone and answers the actual question - [ ] At least one analytical framework was applied (not just mentioned) - [ ] Layer 3 exists and contains 3+ non-obvious insights - [ ] At least 3 adjacent opportunities surfaced (Layer 4) - [ ] Horizon pointers included (Layer 5) - [ ] Red team step completed — counter-arguments are in the document - [ ] Competing perspectives represented - [ ] Sources and confidence levels noted throughout - [ ] Synthesis happened — output contains insights, not just facts - [ ] Document tells a narrative, not a data dump - [ ] Someone with zero knowledge could follow the document - [ ] Someone with deep knowledge would learn something new - [ ] Output is genuinely 10x+ of a surface-level answer - [ ] No section is generic filler - [ ] The "so what" is clear for every major finding --- ## Reference Files Read these BEFORE executing research. They contain detailed methodology that dramatically improves output quality. | File | What it contains | When to read | |---|---|---| | `references/frameworks.md` | 12 analytical frameworks with selection logic and application instructions | Always — select frameworks during planning | | `references/synthesis-engine.md` | Synthesis techniques, red-team methodology, pattern recognition, confidence calibration | Always — use during Step 4 | | `references/playbooks.md` | Step-by-step research playbooks for 6 research categories | Read the relevant playbook for your topic | | `references/source-selection.md` | Source categories, weighting by topic, red flags, conflict resolution | Always — use during source strategy | | `references/examples.md` | Good vs bad output comparisons with commentary | Read when uncertain about quality bar | --- ## What NOT to Do - Never deliver a generic overview and call it research - Never present information without context or "so what" - Never skip Layer 3 — this is where the value lives - Never skip the red team step — unchallenged conclusions are unreliable - Never present a single narrative without acknowledging competing views - Never use filler phrases or hedge unnecessarily - Never dump bullets without synthesis - Never present data without sourcing or confidence framing - Never stop at what was asked — always deliver the 10-15x - Never apply a framework superficially — a checkbox PESTEL is worse than no PESTEL - Never assume the first narrative that emerges is correct --- > Source: [DishantPal/deep-research-skill](https://github.com/DishantPal/deep-research-skill) — distributed by [TomeVault](https://tomevault.io).