tetckx/deep-structural-analysis-skill

Read this in other languages: :cn: 简体中文

Multi-perspective structural analysis skill for complex social, economic, philosophical, and systemic questions. Chinese primary — developed and stress-tested with Chinese corpora; Chinese output quality exceeds English.

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What It Does

Most AI analysis gives you a confident single-perspective answer. This skill does the opposite: it forces the analysis through 16 disciplinary lenses, 10 structural tools, and an attack loop that exposes the analyst's own priors and subjects every conclusion to adversarial review before delivery.

The result is stratified, fact-bound, confidence-calibrated analysis — with blind spots explicitly listed and uncertain claims explicitly marked, instead of hidden.

Use it when the question is complex, multi-sided, or systemic:

• "Why does labor-law enforcement fail so consistently in practice?"
• "What's really driving the AI price war?"
• "Analyze this platform's ecosystem position from multiple angles"
• "用三向分析一下" / "深度分析一下当下的就业形势"

Skip it for factual questions, debugging, summaries, or anything a single source answers.

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What You Get (Example Output)

A real analysis of "Why do city metros keep losing money yet keep expanding?" (example · analyzed 2026-08-08) produces this shape:

Depth: Standard · Complexity domain: Complex

[PRIOR EXPOSED] Default stance: "Persistent metro losses = expansion should stop"
  (to be attacked)
[CORE FINDING] "Loss" is an accounting artifact (high depreciation + statutory
  public-service fares); expansion's hidden benefits (land value uplift, urban
  density, commute costs) never appear on the books
  → a mismatch between the loss narrative and full-cost accounting — not a
  "should we build" question at all
[CROSS-LENS CONSENSUS] Economics (accounting basis vs full-cost) / Institutional
  (statutory fare pricing) / Physical (ridership-density constraints)
[CONFIDENCE] Medium — "accounting basis" is fact (high), "hidden benefits drive
  decisions" is interpretation (medium)
[REVISION TRACE] Counter-evidence "local debt pressure should halt expansion"
  assessed: debt constraint is real but partly offset by land-sale revenue
  → "halt" downgraded to "slow the pace"
[LAYERED IMPACT] System (urban sprawl pattern) / Institutional (subsidy mechanism)
  / Individual (commuter costs)
[BLIND SPOTS] Local fiscal details invisible; shrinking-city cases not covered

Every analysis carries this shape: priors exposed → facts gathered → multi-lens cross-validation → confidence calibrated → revisions from adversarial attack recorded — so you can see what was challenged and what survived, not just the polished conclusion.

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How It Works

1. Trigger Guard — complexity check: is this a structural question or a simple fact?
2. Attack Loop (every run) — expose the analyst's default stance, list counter-evidence before gathering facts, then run an adversarial pass before delivery. Intensity is adjustable: "轻一点" (gentle) / "狠一点" (ruthless).
3. Five Phases — Decompose → Research (web search, mandatory for current events) → Multi-lens Analysis → Structural Tools → Synthesis.
4. 推演四查 (Four Deduction Checks) — competing-hypothesis exclusion, second-order effects, physical anchoring, global-south variables, plus the coexistence check (default: things stay as they are; change needs explicit triggers).
5. Quality Standards — every claim fact-bound; confidence graded (high = 2+ independent sources with opposing stances); numbers carry source & scope; no false balance on power asymmetries.

Key Features

• 16 lenses across Foundation / Human / Structure / Material categories
• 10 structural tools (三向, MLSD, Asymmetry Detection, Incentive Mapping, Strategic Interaction, Reflexivity, ...)
• Attack-loop protocol — the analyst's own priors are exposed and attacked before delivery (not a gimmick: it emerged from adversarial review of this very skill)
• Trauma-sensitive standard — for harm-related topics: no false balance, no "understand the other side" demands on victims
• Gotchas — 9 empirically-verified failure modes (comfort-zone lens selection, dropped history lens, over-fitted signal decoding, ...)
• Behavior-verified — the framework was pruned 590→216 lines based on real-usage trace evidence (now 351 lines after post-1.9.0 additions); the full decision chain is in docs/behavioral-experiment.md

Quick Install

This skill uses the standard SKILL.md format (directory + SKILL.md + references/) and installs into any agent environment that loads skills by that convention:

• OpenCode: Windows %USERPROFILE%\.config\opencode\skills\deep-structural-analysis\; macOS/Linux ~/.config/opencode/skills/deep-structural-analysis/
• DeepSeek Harness (DSH): copy to the project .agents/skills/deep-structural-analysis/ (or the user-level skills directory)
• Claude Code / other SKILL.md-compatible environments: copy to that environment's skills directory (e.g. ~/.claude/skills/)
• Any environment that loads skills by the "directory + SKILL.md" convention works as-is.

Trigger by asking for depth: "深度分析…", "从多个角度…", "用三向…", or "泼冷水/挑刺/反驳我" (adversarial review mode).

Configuration

No external config file — output language follows the user's question language (Chinese question → full Chinese output; English question → full English output; full switching, see "Output Language Rules" in SKILL.md). Depth defaults to Standard; user can specify.

Files

• SKILL.md — core framework (execution, 379 lines)
• docs/depth-reference.md — full theory for 三向 & MLSD (reference)
• docs/behavioral-experiment.md — maintenance decision chain (validation boundaries)
• docs/attack-survivors.md — metacognitive reference (what survives attack)
• docs/case-test-archive.md — case & test archive (maintenance)

Version

v1.9.5 — see docs/UPDATELOG.md for full history (authoritative).

License

MIT