# Researching Deeply > Systematic deep-investigation methodology for auditing codebases, evaluating systems, and researching complex questions. Enforces breadth-first mapping, claim-evidence discipline, cross-artifact consistency checks, and negative-space analysis. Use when the user asks for a deep review, audit, investigation, 深度檢視, 盤點, 稽核, or when a conclusion will drive an important decision. - Skill: `alanlee0323/researching-deeply` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add alanlee0323/researching-deeply` - Raw SKILL.md: https://api.skillmd.com/api/skills/alanlee0323/researching-deeply/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: Alanlee0323 (https://skillmd.com/u/alanlee0323) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/alanlee0323/researching-deeply --- # Researching Deeply Distilled investigation methodology. The goal of any deep research task is not "read a lot" — it is to produce **decision-grade findings**: every claim traceable to evidence, every gap made explicit, ranked by impact. ## When to use this skill - Deep review / audit of a codebase, skill library, config system, or document set. - Research questions where a wrong conclusion is expensive (architecture, investment, migration). - Any task phrased as: 深度檢索, 深度檢視, 全面盤點, 稽核, "is X complete/correct/consistent?". ## Core Principles（順序即優先級） ### 1. Map before you read（先測繪，後深讀） Never start by reading files one-by-one. First build a complete inventory of the terrain: - List every directory, file, and artifact class (`glob`, `ls -R`, `git log --oneline`). - Identify the *artifact types*: source of truth, derived docs, config, indexes, external refs. - Only then choose what to read deeply — and read the **governing documents first** (README, rules, schemas), because they tell you what the system *claims* to be. > Rationale: depth without a map produces anecdotes. The map lets you know when you are done. ### 2. Gather evidence in parallel, conclude in series - Batch independent lookups together (multiple reads/searches per round, not one at a time). - Never form the verdict until the inventory pass is complete — early conclusions bias which evidence you look for afterward (confirmation bias applies to agents too). ### 3. Claim–evidence discipline（主張必附證據） Every finding must carry a pointer that lets someone else re-verify it in under a minute: - Code/docs: `path/to/file.md:42` - Data: value + source + retrieval timestamp - External: URL + accessed date If you cannot attach a pointer, downgrade the claim to a hypothesis and label it as such. ### 4. Cross-artifact consistency check（漂移偵測——最高產出的單一技巧） **Any fact maintained in more than one place will eventually diverge.** This is where the highest-value findings live. Procedure: 1. Enumerate every place a given fact is recorded (e.g., a skill's name may appear in: frontmatter, folder name, routing table, bundle yaml, README index, other skills' text). 2. Extract the value from each location mechanically (grep/script, not memory). 3. Diff them. Every mismatch is a confirmed finding — no judgment call needed. Typical high-yield fact classes: names/identifiers, version numbers, file paths, counts ("we have N modules"), mandatory-rule references, model/API identifiers. ### 5. Negative-space analysis（檢查「應存在而不存在」的東西） Absence is evidence. For each rule or convention the system declares, ask: "if this rule were followed, what artifact would exist?" — then check for it. - Examples: a review gate that mandates audit reports → count actual audit files; a skill referencing a file under its `resources/` folder → verify the file exists; a doc citing a script → verify the script is in the repo. ### 6. Self-application check（系統是否遵守自己的規則） Run the system's own standards against itself. A naming convention document that violates its own naming convention is both a real defect and a signal of missing enforcement. ### 7. Triangulate load-bearing claims（關鍵結論需雙來源） Any claim that the final recommendation depends on needs ≥2 independent confirmations (e.g., code + config, or doc + git history). Single-source claims get a confidence tag. ### 8. Grade confidence explicitly Use a fixed vocabulary so readers can calibrate: - `CONFIRMED` — mechanically verified (diff, existence check, executed command). - `PLAUSIBLE` — inferred from consistent evidence, not directly executed. - `HYPOTHESIS` — pattern-matched; needs verification before acting on it. ### 9. Findings-first reporting（結論先行） Structure the output for the reader, not for your process: 1. **Verdict first** — one paragraph: overall state + the single biggest risk. 2. **Confirmed findings** — each with evidence pointer, ordered by impact (not by discovery order). 3. **Structural gaps** — problems with the *system*, not just instances (an instance is "this link is broken"; the structural gap is "nothing detects broken links"). 4. **Prioritized recommendations** — ordered by leverage: fixes that prevent future defect classes rank above fixes for single defects. ### 10. Know when to stop Stop conditions — declare completion when any holds: - The inventory is fully covered and new samples repeat known finding classes (saturation). - Remaining unknowns cannot change the verdict. - Deeper digging requires inputs only the requester can provide → list them and stop. ## Anti-patterns（紅線） - ❌ Reading files in discovery order instead of governance-first order. - ❌ Reporting findings in the order you found them (order by impact instead). - ❌ "Looks fine" without stating what was checked — always enumerate the checks performed. - ❌ Fixing things mid-investigation when the deliverable is an assessment (report first; change only on request). - ❌ Summary compression that drops evidence pointers — a finding without a pointer is a rumor. ## Output contract ```markdown ## 總評 ## 已確認發現（依影響排序） 1. [CONFIRMED] — 證據: ... ## 結構性缺口 A. ... ## 建議優先順序 1. — ... ``` ## Evolution Log - 2026-07-04 v1.0.0 — Initial capture. Distilled from a full-library drift audit session (Claude Fable 5): the cross-artifact consistency check (#4) and negative-space analysis (#5) produced 7 confirmed findings in that session; codified here for reuse by any agent.