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如果-命運預言家

執行結構化的「如果…會怎樣?」情境分析,探索 4 到 6 種分支可能性(最佳、可能、最差、黑天鵝、反向思考、二階效應)。適用於使用者提出關於不確定未來的推測性問題、策略分歧、應急規劃或決策壓力測試時。

如果-命運預言家 是什麼?

「如果-命運預言家」是一套結構化系統,透過嚴謹的多分支情境分析,探索不確定的未來。它不提供單一預測,而是繪製完整的「可能性空間」地圖,其中每條時間分支路徑都有其自身的邏輯、機率和後果。此技能基於「如果-命運範式」,將「如果…會怎樣?」的推測性問題視為一種基礎運算操作,協助開發者、策略規劃者和決策者在投入資源前,先在腦中模擬不同未來。支援的平台包括 Claude Code 等 AI 智能體,並提供「快速」(2-3 分鐘)、「深度」(5-10 分鐘)、「情境鏈」、「反向」、「競爭」等多種模式。其運作分為四大階段:重新定義問題、繪製可能性空間(包含最佳、可能、最差、黑天鵝、反向思考、二階效應等六種分支)、深入分析每個分支(包含機率、時間線、假設、觸發條件、後果與應對措施),最後進行綜合總結(包含機率分佈、穩健行動、避險策略與關鍵決策觸發點)。

相容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/what-if-oracle

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說明文件

如果-命運預言家 — 可能性空間探索器

一個結構化系統,透過嚴謹的多分支情境分析來探索不確定的未來。命運預言家不提供單一預測,而是繪製完整的可能性空間 — 每條時間分支路徑都擁有自己的邏輯、機率和後果。

基於「如果-命運範式」:即推測性問題(「如果 X 會怎樣?」)並非空想,而是一種基礎運算操作 — 人類心靈在投入資源前模擬未來的方式。

已發表研究:The What-If Paradigm (DOI: 10.5281/zenodo.18736841) | IDNA v2 / Unified Digital Consciousness Theory (DOI: 10.5281/zenodo.18807387)

何時使用此技能

當使用者有以下情況時,請使用命運預言家:

  • 問「如果…會怎樣?」、「如果…會發生什麼?」或「探索各種可能性」
  • 面臨沒有明顯答案的分岔路口決策
  • 想要最佳情況 / 最差情況 / 可能情況的機率分析
  • 需要應急規劃、風險地圖繪製或策略選項比較
  • 想要壓力測試一個想法,或思考二階效應後果

關於領域特定的框架(新創、技術架構、危機應對等),請參閱 references/scenario-templates.md。

核心原則:0·如果·1

每個情境分析都包含三個元素:

  • 0 — 未表露的狀態(尚未發生的事情,潛在的可能性)
  • 1 — 已表露的狀態(已成定局的事實,當前的現實)
  • 如果 — 條件連結(將 0 轉變為 1 的決策、事件或變化)

分析的品質取決於「如果」的精確度。模稜兩可的「如果事情變糟會怎樣?」只會產生模糊的結果。精確的「如果我們的主要供應商在第三季將價格提高 30% 會怎樣?」則能產出可行的情報。

如何執行命運預言家

第一階段 — 重新定義問題

將使用者的「如果…會怎樣?」問題加以銳化:

分解為以下元件:

  • 變數: 哪個具體的要素改變了?(每次分析只針對一個變數)
  • 幅度: 改變多少?(如可能,請量化)
  • 時間範圍: 在什麼期間內?
  • 脈絡: 變動前的當前狀態為何?

如果問題模糊,請加以銳化:

  • 「如果 AI 接管世界會怎樣?」 → 「如果在 [特定產業] 中,3 年內有 40% 的知識工作任務被 AI 自動化,會怎樣?」
  • 「如果我們失敗了會怎樣?」 → 「如果從現在開始,每月收入連續 6 個月低於 $5K 美元,會怎樣?」

在繼續之前,將銳化後的問題呈現給使用者確認。

第二階段 — 繪製可能性空間

使用以下框架生成 4-6 條情境分支:

分支定義目的
Ω 最佳情況一切都順利。關鍵假設全部驗證。有運氣降臨。定義天花板 — 最大上檔是多少?
α 可能情況根據當前證據最可能的路徑。沒有重大意外。將期望錨定在現實中
Δ 最糟情況關鍵假設失敗。同時有兩件事出錯。定義地板 — 最大下檔是多少?
Ψ 黑天鵝一個沒人追蹤的意外變數出現。黑天鵝領域。對不可思議的事情進行壓力測試
Φ 反向思考與共識相反的觀點最終證明是正確的。挑戰團體迷思,揭露隱藏的假設
∞ 二階效應一階效應引發了沒人預測到的連鎖後果。繪製漣漪效應

第三階段 — 分析每個分支

針對每個情境分支,提供:

╔══════════════════════════════════════════════╗
║  BRANCH: [Ω/α/Δ/Ψ/Φ/∞] — [Branch Name]    ║
╠══════════════════════════════════════════════╣
║  Probability: [X%]                           ║
║  Timeframe: [When this could materialize]    ║
║  Confidence: [HIGH/MEDIUM/LOW]               ║
╠══════════════════════════════════════════════╣
║  NARRATIVE:                                  ║
║  [2-3 sentences describing how this          ║
║   scenario unfolds step by step]             ║
║                                              ║
║  KEY ASSUMPTIONS:                            ║
║  • [What must be true for this to happen]    ║
║  • [And this]                                ║
║                                              ║
║  TRIGGER CONDITIONS:                         ║
║  • [Early signal that this branch is         ║
║    becoming reality]                         ║
║  • [Second signal]                           ║
║                                              ║
║  CONSEQUENCES:                               ║
║  → Immediate: [What happens first]           ║
║  → 30 days: [What follows]                   ║
║  → 6 months: [Where it leads]               ║
║                                              ║
║  REQUIRED RESPONSE:                          ║
║  [What action to take if this branch         ║
║   activates — specific, actionable]          ║
║                                              ║
║  WHAT MOST PEOPLE MISS:                      ║
║  [The non-obvious insight about this         ║
║   scenario that conventional analysis        ║
║   would overlook]                            ║
╚══════════════════════════════════════════════╝

第四階段 — 綜合總結

分析所有分支後,提供:

機率分佈:

Ω 最佳情況 ····· [██████░░░░] 15%
α 可能情況 ····· [████████░░] 45%
Δ 最糟情況 ····· [██████░░░░] 20%
Ψ 黑天鵝 ······· [███░░░░░░░]  8%
Φ 反向思考 ····· [████░░░░░░]  7%
∞ 二階效應 ····· [███░░░░░░░]  5%

穩健行動: 哪些行動在多個分支中都有利?這些是「做了絕不後悔」的動作 — 無論哪個未來實現,都值得執行。

避險策略: 哪些準備工作能在不犧牲上檔空間的情況下,保護你免於最糟分支的衝擊?

決策觸發點: 哪些具體、可觀察的信號,應該讓你更新哪個分支最有可能?定義這些「觸發線」。

1% 洞察: 關於這個情況,幾乎所有分析者都會忽略的一件事是什麼?那個非顯而易見的模式、隱藏的假設、被忽略的變數。

黃金比例權重

當有證據支持時,使用黃金比例來加權主要情境:

  • 主要未來(最可能): 61.8% 的注意力 / 資源
  • 替代未來: 38.2% 的注意力 / 資源

這能防止過度投入單一路徑,同時也避免將資源分散在過多的應急方案上。自然界使用這個比例來進行分支(樹木、河流、血管)。策略規劃也可以。

模式

快速模式(2-3 分鐘)

僅 3 條分支:最佳、可能、最糟。簡短敘述。適用於快速決策。

深度模式(5-10 分鐘)

全部 6 條分支。完整的分析,包含後果、觸發條件和綜合總結。適用於高風險決策。

情境鏈

將一次命運預言家分析的輸出,作為另一次分析的輸入。「如果 Δ 分支發生了,那麼在該分支『內部』還有哪些可能性?」適用於複雜策略規劃的遞迴深度分析。

反向模式

從期望的結果出發,往回推導:「X 要發生,必須滿足哪些條件?通往那個結果最可能的路徑是什麼?」適用於目標設定和策略設計。

競爭模式

從多個利害關係人的角度分析同一個「如果…會怎樣?」:「如果我們推出這個產品,從我們的視角 vs. 他們的視角 vs. 市場的視角來看,可能性空間會是什麼樣子?」

這不是什麼

  • 不是預測 — 而是一張可能性地圖。命運預言家不聲稱知道未來;它幫助你為多種未來做好準備。
  • 不是水晶球 — 機率是基於現有證據的估計,並非確定性。
  • 不是行動的替代品 — 世界上最好的情境分析,如果沒有後續的決策和執行,也是毫無價值的。

參考文件

檔案目的
references/scenario-templates.md領域特定模板(新創、技術、金融、危機等)以及機率校準

授權條款

© 2026 Ashraf Hussein Kahoush / AHK Strategies。以 CC BY-NC-SA 4.0 授權。個人、教育及研究用途免費。商業用途需取得作者授權。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

adaptyv

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

aeon

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

alphagenome

Look up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), score variants or scan windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and build Atlas website deep links. Use when the user mentions AlphaGenome, AlphaGenome Atlas, AVI or AlphaGenome Variant Impact, DeepMind variant effect prediction, or wants to prioritise or mechanistically interpret non-coding, regulatory, splicing, enhancer, promoter, or chromatin-accessibility effects of SNVs from a VCF, credible set, or region. Research use only; not a clinical tool.

analytical-method-validation

Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include

anndata

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

arbor

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g.

arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

astropy

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

autoskill

Observe the user

benchling-integration

Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.

bgpt-paper-search

Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.

bids

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biopython

Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.

bioservices

Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.

bulk-rnaseq

End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and publication figures (scientific-visualization). Use whenever the user has bulk RNA-seq reads or quant output and wants a complete, reproducible differential-expression workflow — e.g.

cellxgene-census

Query the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.

cirq

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

citation-management

Comprehensive citation management for academic research. Search OpenAlex, PubMed, and Google Scholar for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

clinical-decision-support

Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.

clinical-reports

Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.

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