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edge-candidate-agent边缘候选 Agent

Agent Skill

edge-candidate-agent 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

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CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:edge-candidate-agent(边缘候选 Agent)
来源仓库:https://github.com/tradermonty/claude-trading-skills
仓库路径:skills/edge-candidate-agent
安装命令:
npx skills add https://github.com/tradermonty/claude-trading-skills --skill edge-candidate-agent
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/tradermonty/claude-trading-skills --skill edge-candidate-agent

简介

edge-candidate-agent 将市场观察转化为结构化研究票据,聚焦信号质量与接口兼容性。

  • 支持每日自动检测 OHLCV 数据异常,输出 Phase I 兼容候选规格,适合量化研究 pipeline。
  • 可作为独立端到端运行,也可嵌入拆分流程最终阶段,服务于策略导出与验证环节。
  • 安装命令:npx skills add https://github.com/tradermonty/claude-trading-skills --skill edge-candidate-agent
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Edge Candidate Agent

Overview

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.

When to Use

  • Convert market observations, anomalies, or hypotheses into structured research tickets.
  • Run daily auto-detection to discover new edge candidates from EOD OHLCV and optional hints.
  • Export validated tickets as strategy.yaml + metadata.json for trade-strategy-pipeline Phase I.
  • Run preflight compatibility checks for edge-finder-candidate/v1 before pipeline execution.

Prerequisites

  • Python 3.9+ with PyYAML installed.
  • Access to the target trade-strategy-pipeline repository for schema/stage validation.
  • uv available when running pipeline-managed validation via --pipeline-root.

Output

  • strategies/<candidate_id>/strategy.yaml: Phase I-compatible strategy spec.
  • strategies/<candidate_id>/metadata.json: provenance metadata including interface version and ticket context.
  • Validation status from scripts/validate_candidate.py (pass/fail + reasons).
  • Daily detection artifacts:

- daily_report.md - market_summary.json - anomalies.json - watchlist.csv - tickets/exportable/*.yaml - tickets/research_only/*.yaml

Position in Split Workflow

Recommended split workflow:

  1. skills/edge-hint-extractor: observations/news -> hints.yaml
  2. skills/edge-concept-synthesizer: tickets/hints -> edge_concepts.yaml
  3. skills/edge-strategy-designer: concepts -> strategy_drafts + exportable ticket YAML
  4. skills/edge-candidate-agent (this skill): export + validate for pipeline handoff

Workflow

  1. Run auto-detection from EOD OHLCV:

- skills/edge-candidate-agent/scripts/auto_detect_candidates.py - Optional: --hints for human ideation input - Optional: --llm-ideas-cmd for external LLM ideation loop

  1. Load the contract and mapping references:

- references/pipeline_if_v1.md - references/signal_mapping.md - references/research_ticket_schema.md - references/ideation_loop.md

  1. Build or update a research ticket using references/research_ticket_schema.md.
  2. Export candidate artifacts with skills/edge-candidate-agent/scripts/export_candidate.py.
  3. Validate interface and Phase I constraints with skills/edge-candidate-agent/scripts/validate_candidate.py.
  4. Hand off candidate directory to trade-strategy-pipeline and run dry-run first.

Quick Commands

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

Export Rules

  • Keep validation.method: full_sample.
  • Keep validation.oos_ratio omitted or null.
  • Export only supported entry families for v1:

- pivot_breakout with vcp_detection - gap_up_continuation with gap_up_detection

  • Mark unsupported hypothesis families as research-only in ticket notes, not as export candidates.

Guardrails

  • Reject candidates that violate schema bounds (risk, exits, empty conditions).
  • Reject candidate when folder name and id mismatch.
  • Require deterministic metadata with interface_version: edge-finder-candidate/v1.
  • Use --dry-run in pipeline before full execution.

Resources

skills/edge-candidate-agent/scripts/export_candidate.py

Generate strategies/<candidate_id>/strategy.yaml and metadata.json from a research ticket YAML.

skills/edge-candidate-agent/scripts/validate_candidate.py

Run interface checks and optional StrategySpec/validate_spec checks against trade-strategy-pipeline.

skills/edge-candidate-agent/scripts/auto_detect_candidates.py

Auto-detect edge ideas from EOD OHLCV, generate exportable/research tickets, and optionally export/validate automatically.

references/pipeline_if_v1.md

Condensed integration contract for edge-finder-candidate/v1.

references/signal_mapping.md

Map hypothesis families to currently exportable signal families.

references/research_ticket_schema.md

Ticket schema used by export_candidate.py.

references/ideation_loop.md

Hint schema and external LLM ideation command contract.

适合场景

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02

需要根据任务场景推荐可安装能力包时

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能力概览

能力 1

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能力 2

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能力 3

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能力 4

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.62%
按下载量换算606

Claude

31.81%
按下载量换算573

Cursor

17.55%
按下载量换算316

Gemini CLI

9.14%
按下载量换算165

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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