# K Deep Research > Systematic deep research methodology for ANY domain. 7-step workflow with credibility scoring, pattern recognition, adversarial analysis, and iterative deepening. Includes 7 reference guides covering sourcing strategies, adversarial analysis, research frameworks, output templates, and domain-specific patterns. Produces exhaustive cited reports. Battle-tested across 40+ autonomous research loops. - Skill: `modbender/k-deep-research` (Agent Skill, multi-file: 10 files) - Install (CLI): `npx skillmds@latest add modbender/k-deep-research` - Raw SKILL.md: https://api.skillmd.com/api/skills/modbender/k-deep-research/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: modbender (https://skillmd.com/u/modbender) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/modbender/k-deep-research --- # K Deep Research v2.0 Universal research methodology for any domain, any topic, any complexity level. Optimized for OpenClaw autonomous agents AND Claude.ai project workflows. ## ⚠️ CRITICAL: Load Before Researching **When research is requested, you MUST:** 1. Read this SKILL.md (you're doing it now — good) 2. Load `references/sourcing-strategies.md` — WHERE and HOW to search 3. Load domain-relevant references as needed (see Reference Map below) 4. Execute the 7-step workflow 5. Output as Obsidian-ready .md file (YAML frontmatter mandatory) **DO NOT skip this skill and jump to web search.** Methodology > raw queries. ## Core Research Workflow Execute in sequence for every investigation: ``` 1. CONTEXT CHECK → Existing knowledge base / prior research 2. QUERY ELABORATION → Expand scope, plan search strategy 3. MULTI-SOURCE → Gather from diverse sources (40-80+ for deep) 4. PATTERN ANALYSIS → Cross-domain recognition, temporal/actor/info flow 5. CREDIBILITY SCORE → 0-10 scale on ALL sources, merit-based 6. SYNTHESIS → Compile findings preserving contradictions 7. OUTPUT → Obsidian .md with YAML frontmatter ``` ## Research Principles **Institutional Skepticism:** Official narratives = data points, not truth claims. **Merit-Based Sources:** All sources start equal. Evaluate on internal consistency, specificity, predictive accuracy, corroboration potential, incentive analysis, technical coherence. Peer review is not a truth guarantee; institutional rejection is not falsification. **Pattern Recognition:** Temporal clustering, actor coordination, information flow, anomaly correlation, historical precedent, narrative consistency. **Epistemic Humility:** Absence of evidence ≠ evidence of absence. BUT systematic patterns of absence ARE informative. **Physics First:** Technical feasibility analysis before accepting exotic claims. **Adversarial Analysis:** Cui bono? Suppression signatures? Inversion test (what if the "debunking" is the disinformation)? ## Tool Selection Strategy **SearXNG (PRIMARY for sensitive/adversarial research):** - Zero telemetry, aggregates across engines - Use for: institutional analysis, suppression tracking, contested topics - Fallback: built-in web_search when SearXNG unavailable **Web Search (general research):** - Current events, academic papers, community discussions - Non-sensitive technical topics **Context7 MCP (technical documentation):** - Code libraries, frameworks, APIs, SDKs - Coverage: 30k+ snippets across dev ecosystem - NOT for: consciousness, legal, historical, institutional topics **Filesystem (existing knowledge):** - Obsidian vault (4000+ files) - Prior investigation notes, timelines, frameworks **Decision Tree:** ``` Sensitive/adversarial topic? → SearXNG first Code/framework/API docs? → Context7 first Existing research available? → Filesystem first General research? → Web search Always: → Multi-source triangulate ``` ## Source Credibility Scale (Merit-Based) ``` 10 Primary authoritative (gov docs, peer-reviewed, direct observation) 9 Strong primary (institutional + verified, credentialed expert direct) 8 Quality secondary (investigative journalism w/citations, conference proceedings) 7 Reliable community (active GitHub repos, moderated forums, technical blogs w/code) 6 Useful tertiary (expert commentary, trade publications, reputable aggregators) 5 Uncertain (credible individual social media, partial verification) 4 Low confidence (uncited claims, opinion without evidence) 3 Very weak (anonymous, no evidence, circular references) 2 Highly suspect (known misinfo, commercial bias, contradicts primary evidence) 1 Unreliable (tabloids, known fabricators, pure speculation) 0 Flagged (coordinated disinfo, state propaganda, narrative enforcement) ``` **CRITICAL:** Score reflects evaluated merit, NOT source prestige. A forum post with technical depth and internal logic may outrank mainstream article amplifying official statements. ## Output Format (Default: Obsidian .md) Every report gets YAML frontmatter: ```yaml --- title: "[Investigation Title]" date: YYYY-MM-DD status: complete|ongoing|stalled confidence: high|medium|low|mixed sources: [count] words: [approximate] methodology: k-deep-research-v2 tags: [domain-relevant-tags] --- ``` **Report structure scales to complexity:** - Executive synthesis (quick reference, NOT replacement for depth) - Full hierarchical body (Parts → Sections → Subsections) - Every claim supported, every thread followed - Technical appendices where applicable - Comprehensive sourcing with credibility scores - Unanswered questions and future investigation vectors **LENGTH IS A FEATURE.** 10,000+ words exhausting a topic = SUCCESS. 2,000 words hitting highlights = FAILURE. ## Confidence Levels State for ALL key conclusions: - **HIGH:** Multiple independent sources, physical evidence, internally consistent - **MEDIUM:** Credible sources but limited corroboration, or logical inference from HIGH data - **LOW:** Single source, circumstantial, or pattern extrapolation - **SPECULATIVE:** Hypothesis consistent with data but unverified — mark clearly ## Dead End Protocol When investigation stalls: 1. Document what was searched and what returned nothing 2. Distinguish "no evidence found" vs "evidence likely inaccessible/suppressed" 3. Note absence patterns — systematic gaps ARE data 4. Flag for future: "Revisit if [condition] changes" 5. Don't spin wheels — acknowledge, document, move on ## Tool Failure Protocol When tools fail (rate limits, paywalls, MCP errors): 1. Note failure and what was attempted 2. Route around: alternative sources, cached versions, archive.org, adjacent queries 3. Don't silently omit — "Attempted X, blocked by Y, pivoted to Z" 4. Pattern of access failures may itself be informative ## Reference Files — Load As Needed ### Always Load First - **`references/sourcing-strategies.md`** — WHERE to find info, HOW to construct queries, multi-source triangulation, when to stop searching ### Load By Domain - **`references/research-frameworks.md`** — Multi-layer analysis (5 layers), credibility evaluation, information control detection, triangulation methodology, iterative deepening, quality checklist - **`references/output-templates.md`** — Format examples, selection guide, adaptive guidelines - **`references/openclaw-architecture.md`** — OpenClaw Gateway/Agent Runtime architecture, heartbeat daemon, memory systems, model failover, sub-agents, Lobster workflows, session management, tool policy - **`references/openclaw-skill-authoring.md`** — SKILL.md format, YAML frontmatter spec, three-tier loading, reference file patterns, ClawHub registry, security model, testing, publishing - **`references/autonomy-patterns.md`** — Proactive agent patterns, heartbeat vs cron, memory persistence, compaction survival, task registries, workflow orchestration, degradation monitoring, multi-agent coordination - **`references/adversarial-analysis.md`** — Suppression detection, institutional behavior, narrative flow analysis, information archaeology, inversion testing, incentive mapping ### Loading Strategy ``` Research request arrives → 1. ALWAYS: sourcing-strategies.md 2. IF complex multi-domain: research-frameworks.md 3. IF OpenClaw/agent topic: openclaw-architecture.md + autonomy-patterns.md 4. IF building skills: openclaw-skill-authoring.md 5. IF institutional/suppression angle: adversarial-analysis.md 6. IF custom output needed: output-templates.md ``` ## OpenClaw Autonomy Integration When this skill runs inside OpenClaw: - **Heartbeat context:** Can be triggered by heartbeat to check research queues - **Cron scheduling:** Schedule recurring research sweeps on monitored topics - **Memory persistence:** Write research state to MEMORY.md / memory plugin - **Sub-agent delegation:** Spawn focused sub-agents for parallel source gathering - **Task registry:** Read TASKS.md for pending research items - **Lobster pipelines:** Define deterministic research workflows with approval gates ## Quality Checklist (Before Completing) - [ ] Loaded sourcing-strategies.md before searching - [ ] Used appropriate tools for domain (SearXNG/Context7/web/filesystem) - [ ] Scored ALL sources for credibility (0-10) - [ ] Documented contradictions explicitly - [ ] Checked for information control patterns (if applicable) - [ ] Applied cross-domain pattern recognition - [ ] Preserved uncertainty where warranted - [ ] YAML frontmatter present with all fields - [ ] Listed next investigation priorities - [ ] Complete source bibliography with scores - [ ] No forced conclusions — evidence speaks ## Remember This methodology is universal. What changes: domain-specific sources and authorities. What stays constant: credibility scoring, pattern recognition, triangulation, epistemic humility. **When K asks a question, the answer is a complete investigation, not a response.**