Agent skill

Edge Signal Aggregator

by tradermonty in tradermonty/claude-trading-skills

Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted…

MITAuto-check passedData & Analytics

Install Edge Signal Aggregator

skills CLI
$ npx skills add tradermonty/claude-trading-skills --skill edge-signal-aggregator -a claude-code

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

GitHub CLI
$ gh skill install tradermonty/claude-trading-skills edge-signal-aggregator --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-signal-aggregator .claude/skills/edge-signal-aggregator && 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-signal-aggregator
GitHub stars
3k
Used in
1 other repo
Token cost
~1.8k tokens
SKILL.md length
346 words
Files
7 (incl. scripts, references, assets)
Skills in repo
74
Repo updated
First seen
Licence
MIT

At a glance

Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted…

  • Works in 4 steps: Gather Upstream Skill Outputs → Run Signal Aggregation → Review Aggregated Dashboard → …
  • Tasks that involve Data cleaning
  • SKILL.md covers Overview, When to Use, Prerequisites and Workflow, plus 3 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Edge Signal Aggregator is an agent skill from tradermonty/claude-trading-skills. Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts, reference files and assets (for example `assets/default_weights.yaml`, `references/signal-weighting-framework.md` and `scripts/aggregate_signals.py`).

It sits in Data & Analytics, covering Data cleaning. 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

  • Tasks that involve Data cleaning

Example prompts

  • “/edge-signal-aggregator”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Gather Upstream Skill Outputs
  2. Run Signal Aggregation
  3. Review Aggregated Dashboard
  4. Act on High-Conviction Signals

What it can do on your machine

Read from SKILL.md and the folder at commit c8d58f0. 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 3 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 Signal Aggregator loads about 1.8k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 68 tokens; SKILL.md has 346 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.5k

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 c8d58f0, republished under its MIT licence (© tradermonty). 346 words, ~1,805 tokens.

Download SKILL.mdSave it as .claude/skills/edge-signal-aggregator/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
edge-signal-aggregator
description
Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.

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_*.json from edge-candidate-agent
  • reports/edge_concepts_*.yaml from edge-concept-synthesizer
  • reports/theme_detector_*.json from theme-detector
  • reports/sector_analyst_*.json from sector-analyst
  • reports/institutional_flow_*.json from institutional-flow-tracker
  • reports/edge_hints_*.yaml from edge-hint-extractor
Step 2: Run Signal Aggregation

Execute the aggregator script with paths to upstream outputs:

bash
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:

bash
python3 skills/edge-signal-aggregator/scripts/aggregate_signals.py \
  --edge-candidates reports/edge_candidate_agent_*.json \
  --weights-config skills/edge-signal-aggregator/assets/custom_weights.yaml \
  --output-dir reports/
Step 3: Review Aggregated Dashboard

Open the generated report to review:

  1. Ranked Edge Ideas - Sorted by composite conviction score
  2. Signal Provenance - Which skills contributed to each idea
  3. Contradictions - Conflicting signals flagged for manual review
  4. Deduplication Log - Merged overlapping themes
Step 4: Act on High-Conviction Signals

Filter the shortlist by minimum conviction threshold:

bash
python3 skills/edge-signal-aggregator/scripts/aggregate_signals.py \
  --edge-candidates reports/edge_candidate_agent_*.json \
  --min-conviction 0.7 \
  --output-dir reports/

Output Format

JSON Report
json
{
  "schema_version": "1.0",
  "generated_at": "2026-03-02T07:00:00Z",
  "config": {
    "weights": {
      "edge_candidate_agent": 0.25,
      "edge_concept_synthesizer": 0.20,
      "theme_detector": 0.15,
      "sector_analyst": 0.15,
      "institutional_flow_tracker": 0.15,
      "edge_hint_extractor": 0.10
    },
    "min_conviction": 0.5,
    "dedup_similarity_threshold": 0.8
  },
  "summary": {
    "total_input_signals": 42,
    "unique_signals_after_dedup": 28,
    "contradictions_found": 3,
    "signals_above_threshold": 12
  },
  "ranked_signals": [
    {
      "rank": 1,
      "signal_id": "sig_001",
      "title": "AI Infrastructure Capex Acceleration",
      "composite_score": 0.87,
      "contributing_skills": [
        {
          "skill": "edge_candidate_agent",
          "signal_ref": "ticket_2026-03-01_001",
          "raw_score": 0.92,
          "weighted_contribution": 0.23
        },
        {
          "skill": "theme_detector",
          "signal_ref": "theme_ai_infra",
          "raw_score": 0.85,
          "weighted_contribution": 0.13
        }
      ],
      "tickers": ["NVDA", "AMD", "AVGO"],
      "direction": "LONG",
      "time_horizon": "3-6 months",
      "confidence_breakdown": {
        "multi_skill_agreement": 0.30,
        "signal_strength": 0.35,
        "recency": 0.22
      }
    }
  ],
  "contradictions": [
    {
      "contradiction_id": "contra_001",
      "description": "Conflicting sector view on Energy",
      "skill_a": {
        "skill": "sector_analyst",
        "signal": "Energy sector bearish rotation",
        "direction": "SHORT"
      },
      "skill_b": {
        "skill": "institutional_flow_tracker",
        "signal": "Heavy institutional buying in XLE",
        "direction": "LONG"
      },
      "resolution_hint": "Check timeframe mismatch (short-term vs long-term)"
    }
  ],
  "deduplication_log": [
    {
      "merged_into": "sig_001",
      "duplicates_removed": ["theme_detector:ai_compute", "edge_hints:datacenter_demand"],
      "similarity_score": 0.92
    }
  ]
}
Markdown Report

The markdown report provides a human-readable dashboard:

markdown
# Edge Signal Aggregator Dashboard
**Generated:** 2026-03-02 07:00 UTC

## Summary
- Total Input Signals: 42
- Unique After Dedup: 28
- Contradictions: 3
- High Conviction (>0.7): 12

## Top 10 Edge Ideas by Conviction

### 1. AI Infrastructure Capex Acceleration (Score: 0.87)
- **Tickers:** NVDA, AMD, AVGO
- **Direction:** LONG | **Horizon:** 3-6 months
- **Contributing Skills:**
  - edge-candidate-agent: 0.92 (ticket_2026-03-01_001)
  - theme-detector: 0.85 (theme_ai_infra)
- **Confidence Breakdown:** Agreement 0.30 | Strength 0.35 | Recency 0.22

...

## Contradictions Requiring Review

### Energy Sector Conflict
- **sector-analyst:** Bearish rotation (SHORT)
- **institutional-flow-tracker:** Heavy buying XLE (LONG)
- **Hint:** Check timeframe mismatch

## Deduplication Summary
- 14 signals merged into 8 unique themes
- Average similarity of merged signals: 0.89

Reports are saved to reports/ with filenames:

  • edge_signal_aggregator_YYYY-MM-DD_HHMMSS.json
  • edge_signal_aggregator_YYYY-MM-DD_HHMMSS.md

Resources

  • scripts/aggregate_signals.py -- Main aggregation script with CLI interface
  • references/signal-weighting-framework.md -- Rationale for default weights and scoring methodology
  • assets/default_weights.yaml -- Default skill weights configuration

Key Principles

  1. Provenance Tracking -- Every aggregated signal links back to its source skill and original reference
  2. Contradiction Transparency -- Conflicting signals are flagged, not hidden, to enable informed decisions
  3. Configurable Weights -- Default weights reflect typical reliability but can be customized per user
  4. Deduplication Without Loss -- Merged signals retain references to all original sources
  5. Actionable Output -- Ranked list with clear tickers, direction, and time horizon for each idea

© 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 6 other files (scripts, references, assets) in skills/edge-signal-aggregator of tradermonty/claude-trading-skills.

  • SKILL.md
  • assets/default_weights.yaml
  • references/signal-weighting-framework.md
  • requirements.txt
  • scripts/aggregate_signals.py
  • scripts/tests/conftest.py
  • scripts/tests/test_aggregate_signals.py

Open the folder on GitHubat commit c8d58f0

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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Questions about Edge Signal Aggregator

What does Edge Signal Aggregator do?

Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted…. Edge Signal Aggregator is an agent skill from tradermonty/claude-trading-skills. Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.

When should I use Edge Signal Aggregator?

Edge Signal Aggregator fits situations like: tasks that involve Data cleaning.

How do I install Edge Signal Aggregator in Claude Code?

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

How do I install Edge Signal Aggregator in Codex?

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

Can I use Edge Signal Aggregator 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-signal-aggregator -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-signal-aggregator, .gemini/skills/edge-signal-aggregator, .github/skills/edge-signal-aggregator and .opencode/skills/edge-signal-aggregator in your project.

What does Edge Signal Aggregator need to run?

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

Does Edge Signal Aggregator 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 Signal Aggregator 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 Signal Aggregator use?

Edge Signal Aggregator 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 Signal Aggregator use?

About 1.8k tokens (SKILL.md is roughly 7.2k 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 1.7k tokens, read only when the agent opens those files.

What are the alternatives to Edge Signal Aggregator?

Skills that share tags, products or a category with Edge Signal Aggregator: Question2report (refraction-ray/xalpha, 2.7k stars), Dingo Verify (MigoXLab/dingo, 757 stars), Data Validation (platonai/Browser4, 1.2k stars) and Pandas Pro (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Edge Signal Aggregator?

tradermonty (a GitHub user) maintains it in tradermonty/claude-trading-skills, which has 2,982 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on October 11, 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.