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Agent skill

Edge Candidate Agent

by tradermonty in tradermonty/claude-trading-skills

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.

MITAuto-check passedResearch & Science

Install Edge Candidate Agent

skills CLI
$ npx skills add tradermonty/claude-trading-skills --skill edge-candidate-agent -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install tradermonty/claude-trading-skills edge-candidate-agent --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/edge-candidate-agent .claude/skills/edge-candidate-agent && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
edge-candidate-agent
GitHub stars
3k
Used in
1 other repo
Token cost
~1.5k tokens
SKILL.md length
401 words
Files
16 (incl. scripts, references)
Skills in repo
74
Repo updated
First seen
Licence
MIT

At a glance

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.

  • Works in 4 steps: skills/edge-hint-extractor:… → skills/edge-concept-synthesizer:… → skills/edge-strategy-designer: concepts… → …
  • Users ask to turn hypotheses/anomalies into reproducible research tickets
  • SKILL.md covers Overview, When to Use, Prerequisites and Output, plus 6 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Edge Candidate Agent is an agent skill from tradermonty/claude-trading-skills. 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.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 19 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/ideation_loop.md` and `references/pipeline_if_v1.md`).

It sits in Research & Science, covering Reproducible research and Trading and backtesting. The repository describes itself as: Claude Code skills for equity investors and traders — market analysis, technical charting, economic calendars, screeners, and trading strategy development. The licence is MIT.

When your agent uses it

  • Users ask to turn hypotheses/anomalies into reproducible research tickets
  • Convert validated ideas into strategy.yaml + metadata.json
  • Preflight-check interface compatibility (edge-finder-candidate/v
  • Before running pipeline backtests

Example prompts

  • “/edge-candidate-agent”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  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

What it can do on your machine

Read from SKILL.md and the folder at commit eab8d5c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 9 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Edge Candidate Agent loads about 1.5k tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 111 tokens; SKILL.md has 401 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~111
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from tradermonty/claude-trading-skills at commit eab8d5c, republished under its MIT licence (© tradermonty). 401 words, ~1,454 tokens.

Download SKILL.mdSave it as .claude/skills/edge-candidate-agent/SKILL.md (or your agent's skills folder). This skill also uses 15 other files; get the full folder from GitHub.
name
edge-candidate-agent
description
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.

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
  2. Load the contract and mapping references:
    • references/pipeline_if_v1.md
    • references/signal_mapping.md
    • references/research_ticket_schema.md
    • references/ideation_loop.md
  3. Build or update a research ticket using references/research_ticket_schema.md.
  4. Export candidate artifacts with skills/edge-candidate-agent/scripts/export_candidate.py.
  5. Validate interface and Phase I constraints with skills/edge-candidate-agent/scripts/validate_candidate.py.
  6. Hand off candidate directory to trade-strategy-pipeline and run dry-run first.
Show full SKILL.md (159 more words)Show less

Quick Commands

Daily auto-detection (with optional export/validation):

bash
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:

bash
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:

bash
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:

bash
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.

© tradermonty, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 15 other files (scripts, references) in skills/edge-candidate-agent of tradermonty/claude-trading-skills.

  • SKILL.md
  • agents/openai.yaml
  • references/ideation_loop.md
  • references/pipeline_if_v1.md
  • references/research_ticket_schema.md
  • references/signal_mapping.md
  • requirements.txt
  • scripts/auto_detect_candidates.py
  • scripts/candidate_contract.py
  • scripts/export_candidate.py
  • scripts/tests/conftest.py
  • scripts/tests/test_auto_detect_candidates.py
  • scripts/tests/test_candidate_contract.py
  • scripts/tests/test_export_candidate.py
  • scripts/tests/test_validate_candidate.py
  • scripts/validate_candidate.py

Open the folder on GitHubat commit eab8d5c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in tradermonty/claude-trading-skills, which our catalogue first saw on October 7, 2026.

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Categories

Questions about Edge Candidate Agent

What does Edge Candidate Agent do?

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. Edge Candidate Agent is an agent skill from tradermonty/claude-trading-skills. 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.

When should I use Edge Candidate Agent?

Edge Candidate Agent fits situations like: users ask to turn hypotheses/anomalies into reproducible research tickets; convert validated ideas into strategy.yaml + metadata.json; preflight-check interface compatibility (edge-finder-candidate/v; before running pipeline backtests.

How do I install Edge Candidate Agent in Claude Code?

Run `npx skills add tradermonty/claude-trading-skills --skill edge-candidate-agent -a claude-code`. Or copy the skill folder (skills/edge-candidate-agent in tradermonty/claude-trading-skills) into .claude/skills/edge-candidate-agent in your project. Claude Code loads it when a task matches its description.

How do I install Edge Candidate Agent in Codex?

Run `npx skills add tradermonty/claude-trading-skills --skill edge-candidate-agent -a codex`. Or copy the skill folder (skills/edge-candidate-agent in tradermonty/claude-trading-skills) into .agents/skills/edge-candidate-agent in your project. Codex loads it when a task matches its description.

Can I use Edge Candidate Agent in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add tradermonty/claude-trading-skills --skill edge-candidate-agent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/edge-candidate-agent, .gemini/skills/edge-candidate-agent, .github/skills/edge-candidate-agent and .opencode/skills/edge-candidate-agent in your project.

What does Edge Candidate Agent need to run?

Going by SKILL.md and its folder, Edge Candidate Agent needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Edge Candidate Agent access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Edge Candidate Agent safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Edge Candidate Agent use?

Edge Candidate Agent is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Edge Candidate Agent use?

About 1.5k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.3k tokens, read only when the agent opens those files.

What are the alternatives to Edge Candidate Agent?

Skills that share tags, products or a category with Edge Candidate Agent: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Figure Style (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Edge Candidate Agent?

tradermonty (a GitHub user) maintains it in tradermonty/claude-trading-skills, which has 2,977 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on October 9, 2026.

Source: tradermonty/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.