Generate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeline Phase I. Use when users ask to turn hypotheses/anomalies into reproducible research tickets, convert validated ideas into `strategy.yaml` + `metadata.json`, or preflight-check interface compatibility (`edge-finder-candidate/v1`) before running pipeline backtests.
npx skills add https://github.com/BaggaT236/AI-Trading-Skills --skill edge-candidate-agent
Convert daily market observations into reproducible research tickets and Phase I-compatible candidate specs.
Prioritize signal quality and interface compatibility over aggressive strategy proliferation.
This skill can run end-to-end standalone, but in the split workflow it primarily serves the final export/validation stage.
strategy.yaml + metadata.json for trade-strategy-pipeline Phase I.edge-finder-candidate/v1 before pipeline execution.PyYAML installed.trade-strategy-pipeline repository for schema/stage validation.uv available when running pipeline-managed validation via --pipeline-root.strategies/<candidate_id>/strategy.yaml: Phase I-compatible strategy spec.strategies/<candidate_id>/metadata.json: provenance metadata including interface version and ticket context.scripts/validate_candidate.py (pass/fail + reasons).daily_report.mdmarket_summary.jsonanomalies.jsonwatchlist.csvtickets/exportable/*.yamltickets/research_only/*.yamlRecommended split workflow:
skills/edge-hint-extractor: observations/news -> hints.yamlskills/edge-concept-synthesizer: tickets/hints -> edge_concepts.yamlskills/edge-strategy-designer: concepts -> strategy_drafts + exportable ticket YAMLskills/edge-candidate-agent (this skill): export + validate for pipeline handoffskills/edge-candidate-agent/scripts/auto_detect_candidates.py--hints for human ideation input--llm-ideas-cmd for external LLM ideation loopreferences/pipeline_if_v1.mdreferences/signal_mapping.mdreferences/research_ticket_schema.mdreferences/ideation_loop.mdreferences/research_ticket_schema.md.skills/edge-candidate-agent/scripts/export_candidate.py.skills/edge-candidate-agent/scripts/validate_candidate.py.trade-strategy-pipeline and run dry-run first.Daily auto-detection (with optional export/validation):
python3 skills/edge-candidate-agent/scripts/auto_detect_candidates.py \
--ohlcv /path/to/ohlcv.parquet \
--output-dir reports/edge_candidate_auto \
--top-n 10 \
--hints path/to/hints.yaml \
--export-strategies-dir /path/to/trade-strategy-pipeline/strategies \
--pipeline-root /path/to/trade-strategy-pipeline
Create a candidate directory from a ticket:
python3 skills/edge-candidate-agent/scripts/export_candidate.py \
--ticket path/to/ticket.yaml \
--strategies-dir /path/to/trade-strategy-pipeline/strategies
Validate interface contract only:
python3 skills/edge-candidate-agent/scripts/validate_candidate.py \
--strategy /path/to/trade-strategy-pipeline/strategies/my_candidate_v1/strategy.yaml
Validate both interface contract and pipeline schema/stage rules:
python3 skills/edge-candidate-agent/scripts/validate_candidate.py \
--strategy /path/to/trade-strategy-pipeline/strategies/my_candidate_v1/strategy.yaml \
--pipeline-root /path/to/trade-strategy-pipeline \
--stage phase1
validation.method: full_sample.validation.oos_ratio omitted or null.pivot_breakout with vcp_detectiongap_up_continuation with gap_up_detectionid mismatch.interface_version: edge-finder-candidate/v1.--dry-run in pipeline before full execution.skills/edge-candidate-agent/scripts/export_candidate.pyGenerate strategies/<candidate_id>/strategy.yaml and metadata.json from a research ticket YAML.
skills/edge-candidate-agent/scripts/validate_candidate.pyRun interface checks and optional StrategySpec/validate_spec checks against trade-strategy-pipeline.
skills/edge-candidate-agent/scripts/auto_detect_candidates.pyAuto-detect edge ideas from EOD OHLCV, generate exportable/research tickets, and optionally export/validate automatically.
references/pipeline_if_v1.mdCondensed integration contract for edge-finder-candidate/v1.
references/signal_mapping.mdMap hypothesis families to currently exportable signal families.
references/research_ticket_schema.mdTicket schema used by export_candidate.py.
references/ideation_loop.mdHint schema and external LLM ideation command contract.
Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for investigational products.
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
Multi-agent research skill for parallel research execution (10 agents, battle-tested with real case studies).
Use this skill when building or modifying Minecraft server plugins for Paper, Spigot, or Bukkit, including plugin.yml setup, commands, listeners, schedulers, player state, team or arena systems, persistent progression, economy or profile data, configuration files, Adventure text, and version-safe API usage. Trigger for requests like "build a Minecraft plugin", "add a Paper command", "fix a Bukkit listener", "create plugin.yml", "implement a minigame mechanic", "add a perk or quest system", or "debug server plugin behavior".
Internal guidance for composing Codex and GPT-5.4 prompts for coding, review, diagnosis, and research tasks inside the Codex Claude Code plugin
Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.
This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable logs, novelty gates, pruning, rollback, PR preparation, and human approval boundaries.
Autonomous multi-round research review loop. Repeatedly reviews using Claude Code via claude-review MCP, implements fixes, and re-reviews until positive assessment or max rounds reached. Use when user says \"auto review loop\", \"review until it passes\", or wants autonomous iterative improvement.
Take baggat236/edge-candidate-agent from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.