Decision Support

Binding sources

• docs/FINAL_SYSTEM_VISION.md — Layer 5 (AI support); feeds structured context for LLM confirm/recommend, not sole execution authority.
• AGENTS.md — advisory only; L7 unchanged; decisions must stay traceable in payloads and journals.

Purpose

Use this skill to keep the repository's advisory decision-support layer coherent, structured, and operator-readable.

In docs/FINAL_SYSTEM_VISION.md, structured decision context feeds Layer 5 (AI support) and observability; this code path must remain advisory per AGENTS.md.

This layer exists to explain decisions, waits, blocks, and safe-operational readiness. It is not a trading engine and it must not become one by accident.

Use When

Use this skill when:

• editing app/decision_support.py
• editing app/decision_support_analytics.py
• editing app/external_context/*
• standardizing advisory provider interfaces for manual/news/social/calendar context inputs
• extending decision_support payloads in journaling or reporting
• surfacing decision-support fields in the operator panel
• deriving journal-based decision-support analytics or stability views
• improving human-readable labels around fusion, edge, validation, or supervisor state
• extending passive learning-readiness or AI-readiness summaries derived from journaled decision-support traces
• keeping learning_snapshot and ai_audit records clearly separated from runtime trading decisions

Do Not Use When

Do not use this skill for:

• changing strategy actions
• changing scorer thresholds or weights
• changing hard risk policy
• auto-enabling AI or experiments
• adding fake predictive claims or guaranteed-edge language

Safety Contract

• Decision support remains advisory only.
• Hard risk, confidence gates, and execution adapters remain authoritative.
• AI stays a validation or interpretation layer, not an uncontrolled trader.
• External context ingestion must degrade safely when absent or invalid.
• External context may enrich explainability and analytics, but it must never trigger or veto execution by itself.
• Provider stacks or source adapters for external context must fail safely to empty and keep the same advisory-only boundary.
• Freshness, expiry, and provider-health fields for external context may be surfaced for operator review, but they must remain descriptive only.
• AI validation may become context-aware for advisory summaries, but it must not become an execution path, risk override, or order generator.
• Passive learning-readiness fields may summarize snapshot quality, label coverage, or shadow insight availability, but they must not enable runtime learning by themselves.
• Passive AI-readiness fields may summarize audit structure, reason coverage, or evidence usefulness, but they must not activate AI runtime authority.
• Probabilistic edge fields must be labeled clearly if heuristic or uncalibrated.
• Journal-derived edge calibration must remain passive evidence only and must never mutate runtime behavior.
• Passive market regime classification may enrich summaries and future evidence review, but it must not alter scoring, risk, or execution behavior.
• Regime-aware evidence breakdowns must stay descriptive and review-oriented; they must not become promotion logic or runtime gating unless a later task adds that explicitly.
• Mode-aware regime comparison must stay an experiment-review aid only and must not alter baseline, experiment, or scoped-trial execution behavior by itself.

Primary Workflow

1. Read AGENTS.md.
2. Inspect the current pipeline boundary that produces or consumes decision-support metadata.
3. Reuse existing journal, dashboard, and operator-panel payloads where possible.
4. Prefer stable, short field names and operator-readable summaries.
5. Prefer journal-derived normalization over runtime rewrites when adding operator analytics.
6. Verify the new layer did not change execution behavior.

Required Checks

• signal_fusion, external_context, ai_validation, probabilistic_edge, pipeline, and supervisor stay logically distinct.
• No new field implies trade approval that the backend does not perform.
• Operator-facing labels are short, human-readable, and low-noise.
• Missing context files or skipped AI calls still produce safe, interpretable output.
• External signals remain optional and must not change execution behavior when empty, invalid, or stale.
• Provider-stack extensions must keep local-file behavior intact while making future source additions explicit and bounded.
• Supervisor readiness stays advisory unless a later task explicitly adds controlled gating.

Expected Output

• Decision Support: what changed in the advisory layer.
• Operator Surface: how it is shown without adding noise.
• Decision Analytics: what became measurable over time.
• Safety Check: explicit note that execution behavior did not change.