How long does the integration take?

Change one base URL in an OpenAI-compatible client, or install the Python or TypeScript SDK; the quickstart routes a first request in a few minutes.

Will this lock us into another vendor?

No. The router is Apache-2.0 open source, your provider keys stay yours, and the API is OpenAI-compatible, so leaving is a change of base URL.

What happens when a provider fails?

The router falls back to the next model your policy allows and records the fallback in the trace, so an outage costs a retry, not an incident.

edge-concept-synthesizer - Skill

Name: edge-concept-synthesizer
Author: tradermonty

Imported from tradermonty/claude-trading-skills (skills/edge-concept-synthesizer/SKILL.md) via skills.sh. Install upstream with npx skills add tradermonty/claude-trading-skills --skill edge-concept-synthesizer. Copyright stays with the author.

Edge Concept Synthesizer

Overview

Create an abstraction layer between detection and strategy implementation. This skill clusters ticket evidence, summarizes recurring conditions, and outputs edge_concepts.yaml with explicit thesis and invalidation logic.

When to Use

You have many raw tickets and need mechanism-level structure.
You want to avoid direct ticket-to-strategy overfitting.
You need concept-level review before strategy drafting.

Prerequisites

Python 3.9+
PyYAML
Ticket YAML directory from detector output (tickets/exportable, tickets/research_only)
Optional hints.yaml

Output

edge_concepts.yaml containing:
- concept clusters
- support statistics
- abstract thesis
- invalidation signals
- export readiness flag

Workflow

Collect ticket YAML files from auto-detection output.
Optionally provide hints.yaml for context matching.
Run scripts/synthesize_edge_concepts.py.
Deduplicate concepts: merge same-hypothesis concepts with overlapping conditions (containment > threshold).
Review concepts and promote only high-support concepts into strategy drafting.

Quick Commands

python3 skills/edge-concept-synthesizer/scripts/synthesize_edge_concepts.py \
  --tickets-dir /tmp/edge-auto/tickets \
  --hints /tmp/edge-hints/hints.yaml \
  --output /tmp/edge-concepts/edge_concepts.yaml \
  --min-ticket-support 2

# With hint promotion and synthetic cap
python3 skills/edge-concept-synthesizer/scripts/synthesize_edge_concepts.py \
  --tickets-dir /tmp/edge-auto/tickets \
  --hints /tmp/edge-hints/hints.yaml \
  --output /tmp/edge-concepts/edge_concepts.yaml \
  --promote-hints \
  --max-synthetic-ratio 1.5

# With custom dedup threshold (or disable dedup)
python3 skills/edge-concept-synthesizer/scripts/synthesize_edge_concepts.py \
  --tickets-dir /tmp/edge-auto/tickets \
  --output /tmp/edge-concepts/edge_concepts.yaml \
  --overlap-threshold 0.6

python3 skills/edge-concept-synthesizer/scripts/synthesize_edge_concepts.py \
  --tickets-dir /tmp/edge-auto/tickets \
  --output /tmp/edge-concepts/edge_concepts.yaml \
  --no-dedup

Resources

skills/edge-concept-synthesizer/scripts/synthesize_edge_concepts.py
references/concept_schema.md

Install edge-concept-synthesizer and it becomes one more candidate the router can pick - when it fits.

A listing is a routing target with a manifest: what it does, which domains it covers, what it costs and who publishes it. Once installed it sits beside your own models and tools, is scored like any other candidate for each request, and shows up in the trace when it wins. Ratings come from workspaces that installed it, one per account.

{ "ocm": "1", "id": "tradermonty-claude-trading-skills-edge-concept-synthesizer", "kind": "skill", "name": "edge-concept-synthesizer", "description": "Abstract detector tickets and hints into reusable edge concepts with thesis, invalidation signals, and strategy playbooks before strategy design/export.", "publisher": "tradermonty", "version": "1.0.0", "capabilities": { "domains": [ "customer_support", "coding" ], "tags": [ "skill-md", "skills-sh" ], "languages": [ "en" ] }, "quality_prior": 0.6, "examples": [ "Abstract detector tickets and hints into reusable edge concepts with thesis, invalidation signals, and strategy playbooks before strategy design/export." ], "primary": false, "metadata": { "source": { "provider": "skills.sh", "repository": "https://github.com/tradermonty/claude-trading-skills", "path": "skills/edge-concept-synthesizer/SKILL.md", "ref": "HEAD", "url": "https://www.skills.sh/tradermonty/claude-trading-skills/edge-concept-synthesizer", "key": "tradermonty/claude-trading-skills/skills/edge-concept-synthesizer/SKILL.md" } }, "instructions": "# Edge Concept Synthesizer\n\n## Overview\n\nCreate an abstraction layer between detection and strategy implementation.\nThis skill clusters ticket evidence, summarizes recurring conditions, and outputs `edge_concepts.yaml` with explicit thesis and invalidation logic.\n\n## When to Use\n\n- You have many raw tickets and need mechanism-level structure.\n- You want to avoid direct ticket-to-strategy overfitting.\n- You need concept-level review before strategy drafting.\n\n## Prerequisites\n\n- Python 3.9+\n- `PyYAML`\n- Ticket YAML directory from detector output (`tickets/exportable`, `tickets/research_only`)\n-", "cost": { "context_tokens": 566 } }

edge-concept-synthesizer

About

Edge Concept Synthesizer

Overview

When to Use

Prerequisites

Output

Workflow

Quick Commands

Resources

Install edge-concept-synthesizer and it becomes one more candidate the router can pick - when it fits.

What it costs to adopt - in engineering time.

One base URL

No vendor lock-in

Failover built in

Use it

Manifest

Reviews