Comprehensive investigation of complex multi-domain narratives (technology, theology, policy, finance). Systematically decouples factual claims from speculation through iterative verification, multi-source comparison, and contradiction resolution. Use for investigations requiring institutional knowledge, historical context, and handling of conflicting incentives.
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Investigates complex narratives spanning multiple domains by systematically verifying claims, resolving contradictions, and mapping institutional lineages.
Parse the narrative into atomic, verifiable claims and assign categories:
Output: Prioritized verification checklist.
Example:
- "May 15, 2026 marks 135 years since Rerum Novarum" -> Historical fact, calculable
- "Chris Olah spoke at Vatican event" -> Requires event documentation
- "Anthropic-xAI partnership" -> Requires corporate filing verification
Evaluate evidence weight using a three-tier system:
| Tier | Sources | Sufficiency |
|---|---|---|
| 1 -- Primary/Institutional | Official press releases, encyclical texts, SEC filings, government records, direct video/audio | Standalone sufficient for factual claims |
| 2 -- Credible Secondary | Established news (NPR, FT, Guardian, NYT), industry analysis, academic journals, trade publications | Require corroboration across independent outlets |
| 3 -- Tertiary/Aggregation | Blog posts, Reddit, reposted content, think tank summaries, commentary | Only for identifying leads; must be verified by Tier 1/2 |
Check if new claims fit within documented institutional patterns:
Resolution metric: Degree of alignment between claimed position and institutional history.
Identify and reconcile apparent paradoxes:
Type A -- Moral/Financial Paradox:
Type B -- Infrastructure/Sovereignty Paradox:
Type C -- Attribution/Knowledge Paradox:
Audit corporate/technical relationships across dimensions:
| Dimension | Method |
|---|---|
| Partnership existence | SEC filings, press releases, company blogs |
| Technical specifics | Compute capacity specs, infrastructure details |
| Financial scale | Deal announcements, investor presentations |
| Geopolitical implications | Government/defense integration tracking |
| Contradiction with stated values | Cross-reference with ethical statements |
Five research cycles building from surface to systemic:
| Cycle | Question | Focus |
|---|---|---|
| 1 | Does it exist? | Document/event existence, person/institution reality, date accuracy |
| 2 | What exactly was claimed? | Exact wording, attendees, positions, primary source substantiation |
| 3 | What contradictions exist? | Tensions between claims and actions, incentive structures |
| 4 | How does this fit history? | Institutional lineage, precedents, novelty assessment |
| 5 | What's the systemic impact? | Geopolitical/financial consequences, ecosystem response, second-order effects |
Trace principles across centuries:
Rerum Novarum (1891) -> Magnifica Humanitas (2026)
- Factory workers -> Content moderators, data miners
- Steam engines -> Neural networks
- Wage exploitation -> Data extraction, algorithmic control
- Duty to protect labor -> Duty to protect human dignity in AI age
- Worker dignity -> Protection against reduction to data points
Method: Identify core principle -> trace through intervening decades -> map to new domain -> document vocabulary evolution.
Confirm direct quotes and public statements:
Verify claims about invisible labor supply chains:
Sources: Labor rights organizations, academic studies, investigative journalism, NGO documentation.
Map where moral teachings collide with strategic interests:
Extract moral logic underlying policy positions:
Babel Model Jerusalem Model
| |
Centralized efficiency Localized collaboration
Data homogenization Human-centered diversity
Optimization as goal Dignity as goal
Humans as productivity units Humans as irreplaceable persons
| |
Technological dominance Technological humility
Each investigation produces:
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概要と使いどころ
Create and maintain AGENTS.md files for project onboarding and grounding. Use when initialized in a new project, when project structure changes significantly, or when creating subfolder-level AGENTS.md files for complex monorepos.
日本語の概要は準備中です。原文の説明を表示しています。
Audit and update an arbitrary codebase to follow the Code.mil federal open-source licensing model (MIT with 17 U.S.C. Section 105 disclaimers and INTENT.md), including CRAN/R package DESCRIPTION setup.
日本語の概要は準備中です。原文の説明を表示しています。
Ultra-compressed communication mode that cuts output tokens while keeping technical accuracy. Levels: lite, full, ultra and the wenyan variants. Use for /caveman, "caveman mode", "talk like caveman", "be brief" or "less tokens".
日本語の概要は準備中です。原文の説明を表示しています。
Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes - Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/spec asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since X".
日本語の概要は準備中です。原文の説明を表示しています。
Reads Copilot conversation history and extracts executive-ready success stories. Use when the user asks to study prior Copilot sessions, summarize what was done before, mine evidence, or quantify time, cost, risk, and priority alignment from GitHub Copilot history in any workspace or repository.
日本語の概要は準備中です。原文の説明を表示しています。
Normalizes documentation and examples for consistency in style, format, and accuracy. Ensures code matches existing patterns and documentation. Use when reviewing docs, examples, or writing new code that should follow established patterns.
日本語の概要は準備中です。原文の説明を表示しています。