edge-candidate-agent

edge-candidate-agent is a skill for Codex from tradermonty/claude-trading-skills. It costs 85 tokens per session (1,314 once invoked), scanned A, original, MIT.

A research assistant for turning observations about US stocks into structured, repeatable research tickets and strategy specifications.

In plain words
What is it for?
Use it to detect candidates from end-of-day price data, organize anomalies or hypotheses, validate compatibility, and export strategy.yaml and metadata.json files.
Why use it?
It turns informal market hypotheses into validated files that another trading-pipeline stage can process, while preserving their source context.

Skill for Codex

Written for Codex: agents/openai.yaml present.

not rated 2.8krepo +31 today A scan Socket: passSnyk: warnSkillSpector: pass 85 tokens original MIT

Good fit Use it to detect candidates from end-of-day price data, organize anomalies or hypotheses, validate compatibility, and export strategy.yaml and metadata.json files.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tradermonty/claude-trading-skills/edge-candidate-agent
About the project

Claude Trading Skills is a collection of Claude Code workflows for individual investors who want structured market analysis, charting, economic-calendar review, screening, trade planning, journaling, and risk management. It is designed for people using long-term investing, ETFs, dividend stocks, and disciplined swing trading, and the catalogue entries package these workflows as skills, agents, commands, settings, and instructions.

tradermonty/claude-trading-skills · 2,813 stars · on GitHub · tradermonty.github.io

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add tradermonty/claude-trading-skills --skill edge-candidate-agent
Clone the repo
git clone --depth 1 https://github.com/tradermonty/claude-trading-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for edge-candidate-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/tradermonty/claude-trading-skills/edge-candidate-agent/github.svg)](https://agentmods.dev/skills/tradermonty/claude-trading-skills/edge-candidate-agent)
Your own site
<a href="https://agentmods.dev/skills/tradermonty/claude-trading-skills/edge-candidate-agent"><img src="https://agentmods.dev/badge/skills/tradermonty/claude-trading-skills/edge-candidate-agent/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for edge-candidate-agent

Your own site · 80×15
<a href="https://agentmods.dev/skills/tradermonty/claude-trading-skills/edge-candidate-agent"><img src="https://agentmods.dev/badge/skills/tradermonty/claude-trading-skills/edge-candidate-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 85 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,314 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 12 Apr 2026
  • Snyk warn 12 Apr 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00085 $0.01314
Opus 5 $0.00043 $0.00657
Sonnet 5 $0.00017 $0.00263
Haiku 4.5 $0.00009 $0.00131

Measured 12d ago against content hash 6c9545ce9662, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

edge-candidate-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 9 executable files (scripts/auto_detect_candidates.py, scripts/candidate_contract.py, scripts/export_candidate.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

skills/edge-candidate-agent/SKILL.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 141 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 141 lines · 85 tokens per session scan A 6c9545ce9662

Subscribe to this mod's changes

edge-candidate-agent is a skill published in the GitHub repository tradermonty/claude-trading-skills (2,813 stars, last pushed today), licensed MIT. It adds 85 tokens to every session and 1,314 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

sector-rotation

An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.

HKUDS/Vibe-Trading · 39 tokens

twitter-reader

Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…

himself65/finance-skills · 161 tokens

chenhao-limit-up

A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.

questflowai/investorskills · 44 tokens

furusato

A Japanese hometown-tax donation manager for furusato nozei, a system where donations to municipalities can qualify for an income-tax or local-tax deduction. It reads donation receipts, stores donation records, and calculates deduction limits.

kazukinagata/shinkoku · 102 tokens

reading-receipt

An image-reading workflow for extracting structured information from receipts, invoices, and hometown-tax donation certificates. It can first extract text from PDFs and otherwise read their images.

kazukinagata/shinkoku · 64 tokens

vectorbt

High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics.

agiprolabs/claude-trading-skills · 20 tokens