What is edge-candidate-agent?

A tool that turns market observations, unusual events, or hypotheses into research tickets and structured strategy files for a later trading pipeline. A research ticket is a reproducible description of an idea to investigate.

Use it to discover US stock long-side ideas, prioritize them, export strategy.yaml and metadata.json files, and check compatibility with the target pipeline.

It reduces the gap between noticing a possible trading pattern and preparing it for consistent research and validation.

edge-candidate-agent — Skill for Codex by BaggaT236

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 BaggaT236/AI-Trading-Skills --skill edge-candidate-agent

Clone the repo

git clone --depth 1 https://github.com/BaggaT236/AI-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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/edge-candidate-agent/github.svg)](https://agentmods.dev/skills/baggat236/ai-trading-skills/edge-candidate-agent)

Your own site

<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/edge-candidate-agent"><img src="https://agentmods.dev/badge/skills/baggat236/ai-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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/edge-candidate-agent"><img src="https://agentmods.dev/badge/skills/baggat236/ai-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.

Origin 100% copy Near-identical to another mod 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.

Model	Per session	Once 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

This is a copy

100% identical to edge-candidate-agent — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

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:

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

Workflow

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
Load the contract and mapping references:
- references/pipeline_if_v1.md
- references/signal_mapping.md
- references/research_ticket_schema.md
- references/ideation_loop.md
Build or update a research ticket using references/research_ticket_schema.md.
Export candidate artifacts with skills/edge-candidate-agent/scripts/export_candidate.py.
Validate interface and Phase I constraints with skills/edge-candidate-agent/scripts/validate_candidate.py.
Hand off candidate directory to trade-strategy-pipeline and run dry-run first.

Read the full file on GitHub · 141 lines

Files

What ships with it

14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

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 BaggaT236/AI-Trading-Skills (121 stars, last pushed 8d ago), 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. It is 100% identical to edge-candidate-agent, differing in 0 lines, and is treated as a copy.

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