agent-evaluator

Deterministic custom subagent selection helper. Use when you need a reproducible, auditable decision on which custom subagents to activate for a user query (runs scripts/agent_evaluator.py).

Ir a la instalación

Repositorio: arisng/github-copilot-fc
Última actividad en el origen: 3 de abril de 2026 a las 05:13
Idioma detectado de SKILL.md: inglés
Estrellas: 5
Forks: 0

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

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npx skills add https://github.com/arisng/github-copilot-fc --skill agent-evaluator

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¿Prefieres una copia local? Descarga los archivos que SkillsMP tiene disponibles ahora.

Revisa los archivos de origen

Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

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6 archivos

SKILL.md

Instrucciones de origen · Vista previa de solo lectura

name	agent-evaluator
description	Deterministic custom subagent selection helper. Use when you need a reproducible, auditable decision on which custom subagents to activate for a user query (runs scripts/agent_evaluator.py).

Agent Evaluator

Evaluate a user query against the workspace's available subagents and return a JSON decision payload (activated/required/suggested agents and scoring).

Mechanism

Run the evaluator script (located in scripts folder relative this skill file) with the user query as an argument.:

python scripts/agent_evaluator.py "YOUR_QUERY_HERE"

Optional: include a contextual file path as the second argument:

python scripts/agent_evaluator.py "YOUR_QUERY_HERE" "path/to/file.ext"

Output

Writes a JSON object to stdout.
Key fields include:
- activated_agents
- required_agents
- suggested_agents
- evaluations (per-agent score + reasoning)

Examples

Evaluate a query:

python scripts/agent_evaluator.py "Please help refine our custom instruction file"

Evaluate a query with file context:

python scripts/agent_evaluator.py "Update this instruction" "instructions/agent-forced-eval.instructions.md"

Volver a la búsqueda Volver a la instalación

machina-driving

Drive a Machina state machine to a deterministic outcome on behalf of an AI agent. USE WHEN: a task is governed by a state machine (a machine definition JSON with states, transitions, guards, actions, tools, checks, scenarios); executing a workflow that must be deterministic and auditable; running a skill whose SKILL.md declares a `machina:` machine; firing events, checking status, satisfying evidence checks, verifying run integrity with `check`, or producing a terminal report for a machine-driven run; escalating a STUCK run to a human conductor. DO NOT USE FOR: authoring or scoring machine definitions (use machina-authoring); modifying the Machina simulator app or its engine (use machina-simulator); implementing or modifying the driver/runtime tooling such as `scripts/machine-driver.py` (engine development) unless you are a maintainer of this skill upgrading the driver itself; general diagramming or XState/SCXML authoring.

2026-09-09

machina-authoring

Author valid, high-scoring state machines in Machina machine JSON format (spec v3.0.0 / v2.0.0 / v1.0.0). USE WHEN: writing or generating a machine definition (states, transitions, guards, actions, context, scenarios); modeling a real workflow (order fulfillment, refunds, signup, retries) as a Machina state machine; fixing or upgrading a machine JSON for validation or higher compliance score; explaining validation failures or low scores; running the bundled machina-validator.py CLI to validate, score, or generate gaps/scenarios; adding retry guards or cycle protection; or preparing machines for the compliance scorer ("Excellent" ≥90). DO NOT USE FOR: modifying the Machina simulator app, its engine, UI, or SPEC_REGISTRY (use machina-simulator-maintenance), debugging machine-validator.py scripts, XState config authoring, SCXML documents, or general diagramming.

2026-09-08

copilot-cli-subsession

Spawn a new isolated Copilot CLI sub-session from a main Copilot CLI or VS Code session. Use when you need to programmatically create, resume, or chain Copilot CLI sessions with explicit control over session ID, custom agent, model, BYOK provider profile, permissions, and working directory. Triggers: "invoke copilot", "spawn copilot", "copilot sub-session", "programmatic copilot cli", "programmatic copilot subprocess", "copilot cli session id", "resume copilot session", "chain copilot sessions", "isolated copilot session", "subagent copilot cli", "task copilot cli".

2026-09-07

Ver todos los de este repositorio

name	agent-evaluator
description	Deterministic custom subagent selection helper. Use when you need a reproducible, auditable decision on which custom subagents to activate for a user query (runs scripts/agent_evaluator.py).

Agent Evaluator

Evaluate a user query against the workspace's available subagents and return a JSON decision payload (activated/required/suggested agents and scoring).

Mechanism

Run the evaluator script (located in scripts folder relative this skill file) with the user query as an argument.:

python scripts/agent_evaluator.py "YOUR_QUERY_HERE"

Optional: include a contextual file path as the second argument:

python scripts/agent_evaluator.py "YOUR_QUERY_HERE" "path/to/file.ext"

Output

Writes a JSON object to stdout.
Key fields include:
- activated_agents
- required_agents
- suggested_agents
- evaluations (per-agent score + reasoning)

Examples

Evaluate a query:

python scripts/agent_evaluator.py "Please help refine our custom instruction file"

Evaluate a query with file context:

python scripts/agent_evaluator.py "Update this instruction" "instructions/agent-forced-eval.instructions.md"

agent-evaluator

Datos de origen

Opciones de instalación

Revisa los archivos de origen

Agent Evaluator

Mechanism

Output

Examples

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Agent Evaluator

Mechanism

Output

Examples

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