Any dollar-value decision, just Abel it. Finance and crypto nodes are the signal layer (the graph's proxy vocabulary), not the product.
Do not use Abel for pure fact lookup, news recap, or operational how-to when no real decision is being made; use normal retrieval first. Abel starts when the user needs a causal read on a choice that allocates money, time, career-capital, or downside risk.
Install And Authorization
If the user installs this skill, asks to connect Abel, or the workflow is missing an Abel API key, follow references/setup-guide.md exactly.
- - Start the Abel agent OAuth handoff immediately instead of asking for manual credentials.
- Return
data.authUrl to the user, not the /authorize/agent API URL. - Store
data.resultUrl or data.pollToken, ask the user to reply once Google authorization is complete, and only then poll until the result is authorized, failed, or expired. - Persist the resulting
data.apiKey in session state and .env.skill when local storage is available. - Do not continue to live CAP probing until that key is present.
- Never ask the user to paste an email address or Google OAuth code.
Step 1: Preflight + Classify
Confirm auth via references/probe-usage.md. If no Abel key is available, stop and follow references/setup-guide.md.
Classify the request as:
- -
direct_graph for specific ticker/node/path/intervention questions - INLINECODE12 for real-world decisions with no direct node
Horizon gate: If the decision horizon is >3 years ("5年后", "未来十年"), switch to structural mode: web is PRIMARY, graph is VALIDATOR ONLY, and you should not use momentum-style observe as the main loop.
Unstable-premise gate: If the opportunity thesis depends on a recent leak, launch, partnership, shutdown, org change, or other freshness-sensitive claim, do one minimal premise-verification search before L0. Use a Tier A source when possible, or a clearly sourced Tier B report if no primary source exists yet. If the premise is still unanchored, rewrite the task as conditional analysis ("if this is true, where are the opportunities?") and say so before continuing. This gate does not cancel Abel; it decides whether the rest of the read is fact-anchored or conditional. Separate verifiable subclaims from inferred motive/strategy claims, and keep inferences labeled as inference even when some facts are anchored.
Opportunity-scope gate: If the user asks a broad question such as "有什么赚钱机会", lock the primary opportunity frame before L0. Distinguish at least among public-market trade, supplier/competitor scan, startup or B2B opportunity, and career/business opportunity. If the user does not specify, default to public-market trade and label other frames as secondary unless they materially change the answer. If multiple frames matter, label them explicitly instead of mixing them into one undifferentiated mechanism list.
If direct_graph, switch to references/routes/direct-graph.md as the active workflow. Return here only for shared web-grounding and write-up rules.
Step 2: Generate Hypotheses (proxy_routed, L0)
Generate 4-6 candidate causal mechanisms:
- - The obvious mechanism
- A second-order mechanism
- A contrarian (what would make the opposite true?) — REQUIRED
- A confounder (third factor explaining both)
Each mechanism: cause → (transmission) → outcome with a testable proxy and falsification condition.
If the contrarian or confounder is missing, stop and fix that before moving on.
Step 3: Screen + Discover (L0.5)
Map the mechanisms to graph nodes and separate them into:
- - structurally supported
- weakly connected
- narrative-only
Required passes:
- - run structural discovery deeply enough to identify a real transmission chain, not just co-movement
- if the graph only confirms L0, actively search for the strongest graph-based contradiction
- do not declare graph-sparse until capillary discovery is exhausted
Follow the full proxy_routed loop in references/routes/proxy-routed.md.
Step 4: Observe + Verify (L1 + L2)
Observe the key nodes for directional coherence and driver consistency.
Intervene only along real graph-supported edges when a meaningful target exists. Match horizon_steps to the decision window and widen in tiers via references/probe-usage.md when needed.
Aggregate to one directional signal per dimension. Never carry raw prediction decimals into the verdict.
Detailed probe shapes and proxy_routed execution rules live in:
- - INLINECODE21
- INLINECODE22
Step 5: Web Grounding (proxyrouted, or directgraph when freshness matters)
Minimum 4 searches:
- 1. What's happening now — latest prices, policy, events, dates
- Supporting evidence — confirms graph-backed verdict
- Contradicting evidence — actively search for why verdict is WRONG (mandatory)
- User-perspective — what a real buyer/decision-maker would search (second-hand prices, waitlists, real experiences)
Contradicting evidence is mandatory. Stop only after you know whether key time-sensitive claims do or do not have a primary-source anchor.
Follow references/web-grounding.md for source hierarchy, wording, and return-to-graph rules.
Graph findings (L2) take precedence over web (L0) in the verdict. Exception: graph-sparse dimensions, where web is primary with lower confidence.
Step 5.5: Personalize
Before writing, check agent memory/context for user profile (income, experience, risk tolerance, life stage, goals). If available, tailor the action layer to that person. If not, give universal advice and say what user details would sharpen the read.
The causal graph is universal. The verdict is personal.
Step 6: Write Report
Read assets/report-guide.md and references/rendering.md before writing.
Render gate (MANDATORY): apply the label-pass and guard workflow from references/rendering.md before finalizing. For non-asset or proxy_routed questions, raw tickers, raw node ids, graph paths, signed prediction decimals, and rendering scratch work stay out of visible prose.
Output default (MANDATORY): default to main answer only. Do not emit an appendix, trace block, evidence dump, rendering scratch work, or probe/process transcript unless the user explicitly asks for evidence details, debug output, reproducibility steps, or a trace.
Write the final answer to the contract in assets/report-guide.md.
Keep claim-strength honesty explicit: life decisions are graph-grounded advice, not causal proof.
References (read only when needed)
- -
references/routes/direct-graph.md — ticker question routing - INLINECODE30 — proxy-routed graph workflow
- INLINECODE31 — OAuth install (only if key missing)
- INLINECODE32 — exact
cap_probe.py command shapes - INLINECODE34 — label-pass rules, visible/internal split, guard usage
- INLINECODE35 — full output contract with archetypes, rendering rules, coverage areas
技能名称: causal-abel
详细描述:
任何涉及金钱的决策,交给Abel就好。金融和加密节点是信号层(图谱的代理词汇),而非最终产品。
当用户并未做出实际决策时,不要将Abel用于纯粹的事实查询、新闻摘要或操作指南;请优先使用常规检索。只有当用户需要对涉及资金、时间、职业资本或下行风险的选择进行因果解读时,才启用Abel。
安装与授权
如果用户安装此技能、要求连接Abel,或工作流缺少Abel API密钥,请严格遵循references/setup-guide.md。
- - 立即启动Abel代理OAuth交接,而非要求手动输入凭证。
- 向用户返回data.authUrl,而非/authorize/agent API URL。
- 存储data.resultUrl或data.pollToken,要求用户在Google授权完成后回复,然后轮询直至结果为authorized、failed或过期。
- 将生成的data.apiKey持久化到会话状态中,并在本地存储可用时保存至.env.skill。
- 在密钥就绪前,不要继续实时CAP探测。
- 切勿要求用户粘贴电子邮件地址或Google OAuth代码。
步骤1:预检与分类
通过references/probe-usage.md确认授权状态。若无Abel密钥可用,则停止并遵循references/setup-guide.md。
将请求分类为:
- - directgraph:针对特定代码/节点/路径/干预措施的问题
- proxyrouted:无直接节点的现实世界决策
时间跨度门控: 若决策时间跨度超过3年(如“5年后”、“未来十年”),切换至结构模式:网络为主,图谱仅作验证,且不应将动量式观测作为主循环。
前提不稳定门控: 若机会论点依赖于近期泄露、发布、合作、关闭、组织变动或其他时效性强的声明,则在L0之前进行一次最小化的前提验证搜索。尽可能使用A级来源,若无主要来源,则使用来源清晰的B级报告。若前提仍无法锚定,则将任务重写为条件分析(“如果这是真的,机会在哪里?”),并在继续前说明。此门控不取消Abel;它决定后续解读是基于事实锚定还是条件假设。将可验证的子声明与推断的动机/策略声明分开,即使部分事实已锚定,也要将推断标注为推断。
机会范围门控: 若用户提出宽泛问题(如“有什么赚钱机会”),则在L0之前锁定主要机会框架。至少区分公开市场交易、供应商/竞争对手扫描、初创或B2B机会、以及职业/商业机会。若用户未指定,默认设为公开市场交易,并将其他框架标注为次要,除非它们实质性改变答案。若多个框架都重要,则明确标注,而非混入一个无差别的机制列表。
若为direct_graph,则切换至references/routes/direct-graph.md作为活动工作流。仅当涉及共享的网络锚定和报告撰写规则时,才返回此处。
步骤2:生成假设(proxy_routed,L0)
生成4-6个候选因果机制:
- - 显而易见的机制
- 二阶机制
- 反向机制(什么会使结果相反?)—— 必选项
- 混杂因素(解释两者的第三方因素)
每个机制:原因 → (传导) → 结果,附带可测试的代理变量和证伪条件。
若缺少反向机制或混杂因素,则停止并修正后再继续。
步骤3:筛选与发现(L0.5)
将机制映射到图谱节点,并分为:
必需步骤:
- - 充分深入结构发现,以识别真实的传导链,而非仅仅是协同变动
- 若图谱仅确认L0,则主动搜索基于图谱的最强矛盾
- 在穷尽毛细血管级发现之前,不得宣布图谱稀疏
遵循references/routes/proxy-routed.md中的完整proxy_routed循环。
步骤4:观测与验证(L1 + L2)
观测关键节点的方向一致性和驱动因素一致性。
仅在存在有意义目标且沿真实图谱支持的边进行干预。将horizon_steps与决策窗口匹配,并在需要时通过references/probe-usage.md按层级放宽。
每个维度聚合为一个方向信号。切勿将原始预测小数带入最终裁决。
详细的探测形状和proxy_routed执行规则位于:
- - references/routes/proxy-routed.md
- references/probe-usage.md
步骤5:网络锚定(proxyrouted,或时效性重要的directgraph)
至少进行4次搜索:
- 1. 当前情况 — 最新价格、政策、事件、日期
- 支持证据 — 确认图谱支持的裁决
- 矛盾证据 — 主动搜索为何裁决是错误的(必选项)
- 用户视角 — 真实买家/决策者会搜索的内容(二手价格、等候名单、真实体验)
矛盾证据为必选项。只有在确认关键时效性声明是否有主要来源锚定后,方可停止。
遵循references/web-grounding.md中的来源层级、措辞和返回图谱规则。
图谱发现(L2)在裁决中优先于网络发现(L0)。例外:图谱稀疏维度,此时网络为主,置信度较低。
步骤5.5:个性化
在撰写前,检查代理记忆/上下文中的用户画像(收入、经验、风险承受能力、人生阶段、目标)。若可用,则将行动层定制化到该用户。若不可用,则给出通用建议,并说明哪些用户细节能优化解读。
因果图谱是通用的。裁决是个性化的。
步骤6:撰写报告
在撰写前,阅读assets/report-guide.md和references/rendering.md。
渲染门控(必选项): 在定稿前,应用references/rendering.md中的标签传递和防护工作流。对于非资产或proxy_routed问题,原始代码、原始节点ID、图谱路径、带符号的预测小数以及渲染草稿不得出现在可见文本中。
输出默认(必选项): 默认仅输出主要答案。除非用户明确要求证据细节、调试输出、可复现步骤或追踪信息,否则不得输出附录、追踪块、证据转储、渲染草稿或探测/过程记录。
根据assets/report-guide.md中的契约撰写最终答案。
保持声明强度的诚实性:人生决策是基于图谱的建议,而非因果证明。
参考资料(仅在需要时阅读)
- - references/routes/direct-graph.md — 代码问题路由
- references/routes/proxy-routed.md — 代理路由图谱工作流
- references/setup-guide.md — OAuth安装(仅在密钥缺失时)
- references/probe-usage.md — 精确的cap_probe.py命令格式
- references/rendering.md — 标签传递规则、可见/内部拆分、防护使用
- assets/report-guide.md — 包含原型、渲染规则、覆盖范围的完整输出契约