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.
npx skills add BaggaT236/AI-Trading-Skills --skill edge-signal-aggregatorgit clone --depth 1 https://github.com/BaggaT236/AI-Trading-SkillsWrote 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.
[](https://agentmods.dev/skills/baggat236/ai-trading-skills/edge-signal-aggregator)<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/edge-signal-aggregator"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/edge-signal-aggregator/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.
<a href="https://agentmods.dev/skills/baggat236/ai-trading-skills/edge-signal-aggregator"><img src="https://agentmods.dev/badge/skills/baggat236/ai-trading-skills/edge-signal-aggregator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00054 | $0.01840 |
| Opus 5 | $0.00027 | $0.00920 |
| Sonnet 5 | $0.00011 | $0.00368 |
| Haiku 4.5 | $0.00005 | $0.00184 |
Grade A, and why
edge-signal-aggregator 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 13d ago.
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.
This is a copy
100% identical to edge-signal-aggregator — 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.
How it starts
The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Edge Signal Aggregator
Overview
Combine outputs from multiple upstream edge-finding skills into a single weighted conviction dashboard. This skill applies configurable signal weights, deduplicates overlapping themes, flags contradictions between skills, and ranks composite edge ideas by aggregate confidence score. The result is a prioritized edge shortlist with provenance links to each contributing skill.
When to Use
- After running multiple edge-finding skills and wanting a unified view
- When consolidating signals from edge-candidate-agent, theme-detector, sector-analyst, and institutional-flow-tracker
- Before making portfolio allocation decisions based on multiple signal sources
- To identify contradictions between different analysis approaches
- When prioritizing which edge ideas deserve deeper research
Prerequisites
- Python 3.9+
- No API keys required (processes local JSON/YAML files from other skills)
- Dependencies:
pyyaml(standard in most environments)
Workflow
Step 1: Gather Upstream Skill Outputs
Collect output files from the upstream skills you want to aggregate:
reports/edge_candidate_*.jsonfrom edge-candidate-agentreports/edge_concepts_*.yamlfrom edge-concept-synthesizerreports/theme_detector_*.jsonfrom theme-detectorreports/sector_analyst_*.jsonfrom sector-analystreports/institutional_flow_*.jsonfrom institutional-flow-trackerreports/edge_hints_*.yamlfrom edge-hint-extractor
Step 2: Run Signal Aggregation
Execute the aggregator script with paths to upstream outputs:
python3 skills/edge-signal-aggregator/scripts/aggregate_signals.py \
--edge-candidates reports/edge_candidate_agent_*.json \
--edge-concepts reports/edge_concepts_*.yaml \
--themes reports/theme_detector_*.json \
--sectors reports/sector_analyst_*.json \
--institutional reports/institutional_flow_*.json \
--hints reports/edge_hints_*.yaml \
--output-dir reports/
Optional: Use a custom weights configuration:
What ships with it
5 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.
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.
- 13d ago First seen · 219 lines · 54 tokens per session scan A dd859ce2c932
edge-signal-aggregator is a skill published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 9d ago), licensed MIT. It adds 54 tokens to every session and 1,840 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to edge-signal-aggregator, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
ib-pmcc-advisor
Analyze PMCC (Poor Man's Covered Call / diagonal spread) positions from IB portfolio. For each diagonal spread, reports short leg risk (delta, IV, assignment probability), daily P&L projections, top-3 roll candidates, and a side-by-side comparison table. Requires TWS or IB Gateway running locally.
scanner-pmcc
Scan stocks for Poor Man's Covered Call (PMCC) suitability. Analyzes LEAPS and short call options for delta, liquidity, spread, IV, yield, trend direction, and earnings proximity. Use when user asks about PMCC candidates, diagonal spreads, or LEAPS strategies.
ib-stop-loss
Downside stop-loss management for PMCC, naked LEAPS, and stock positions in IB. Computes stop prices, detects alerts, and places conditional combo orders. Dry-run by default. Requires TWS or IB Gateway running locally.
ib-trailing-stop
Server-side trailing stop management for stocks and naked LEAPS in IB. Places native TRAIL orders that auto-ratchet the stop as price climbs. Dry-run by default. Requires TWS or IB Gateway running locally.
stock_analyzer
A stock and market analysis skill that returns structured information about trends, prices, news, risks, catalysts, and possible trading plans.
ib-collar
Generate tactical collar strategy reports for protecting PMCC positions through earnings or high-risk events. Requires TWS or IB Gateway running locally.