crypto-market-research-skill

name	crypto-market-research-skill
description	Market-specific financial research skill for crypto asset and crypto derivatives analysis. Use when Codex needs to parse crypto research, analyze market structure, funding, open interest, liquidity, order-book context, on-chain evidence, macro-liquidity narratives, factor hypotheses, or trade-plan evidence with source-traced reasoning.

Crypto Market Research Skill

Operating Rule

Keep the market boundary first. Treat report parsing, factor engineering, and philosophical analysis as output modes inside crypto market research, not separate top-level skills.

Preserve source-project capability before optimizing. When adapting a workflow, check references/source-map.md and references/capability-matrix.md so the result remains traceable to pinned upstream snapshots.

Output Modes

Choose one mode explicitly when the user has not specified the expected output:

• report_parsing: extract, normalize, and explain content from research notes, protocol reports, market updates, exchange data, news, papers, or datasets.
• factor_engineering: turn structured research evidence into factor candidates with evidence, calculation logic, economic hypothesis, and validation status.
• philosophical_analysis: apply explicit thinking models to examine assumptions, causality, uncertainty, failure modes, and market narratives.

Workflow

1. Identify the research market as crypto and reject A-share assumptions unless the user is comparing markets.
2. Select the output mode and load only the relevant reference files.
3. Run scripts/source_doctor.py when live data sources are needed or when prior source health is unknown.
4. Gather or inspect source evidence before forming conclusions.
5. Run the Source Health Loop: detect failed, stale, partial, or suspicious sources and surface diagnostic events before conclusions.
6. Apply the QuantMind Layer pattern: convert raw or semi-structured material into structured research evidence.
7. If producing factor work, output factor candidates with the required factor contract.
8. If applying thinking models, use progressive disclosure: start with the smallest relevant model subset and expand only when the question requires it.
9. State data freshness, source limitations, fallback sources, repair candidates, and validation status.

Factor Candidate Contract

Every factor candidate must include:

• factor_name
• market
• asset_universe
• source_evidence
• calculation_logic
• economic_hypothesis
• validation_status

Do not present a candidate factor as a final trading signal without validation evidence.

References

• references/source-map.md: pinned upstream source repositories and capability provenance.
• references/capability-matrix.md: source parity, market adaptation, and quality upgrade checklist.
• references/data-source-policy.md: crypto source categories, fallback rules, and freshness handling.
• references/source-health-loop.md: diagnostic events, user warnings, fallback handling, and self-repair candidates for data-source problems.
• references/market-structure-policy.md: crypto market-structure evidence requirements.
• references/quantmind-layer.md: structured evidence extraction contract inspired by QuantMind.
• references/research-output-modes.md: output mode definitions and selection rules.
• references/thinking-model-adapter.md: progressive disclosure rules for thinking models.
• scripts/source_doctor.py: executable OpenCLI source health checker that emits diagnostic events and repair candidates.