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 mphinance/alpha-skills --skill edge-signal-aggregatorgit clone --depth 1 https://github.com/mphinance/alpha-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/mphinance/alpha-skills/edge-signal-aggregator)<a href="https://agentmods.dev/skills/mphinance/alpha-skills/edge-signal-aggregator"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-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/mphinance/alpha-skills/edge-signal-aggregator"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-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 11d 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
6 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.
- 11d ago First seen · 219 lines · 54 tokens per session scan A dd859ce2c932
edge-signal-aggregator is a skill published in the GitHub repository mphinance/alpha-skills (22 stars, last pushed 13d 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.
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