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).

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Dépôt: arisng/github-copilot-fc
Dernière activité de la source: 3 avril 2026 à 05:13
Langue détectée de SKILL.md: anglais
Étoiles: 5
Forks: 0

Options d'installation

Le prompt qui vérifie d'abord la source est sélectionné par défaut. Vous pouvez passer à une commande directe ou télécharger une copie locale.

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

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Lisez SKILL.md et les fichiers associés affichés par SkillsMP avant de décider de l'installer.

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6 fichiers

SKILL.md

Instructions source · Aperçu en lecture seule

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"

Retour à la recherche Retour à l’installation

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

Tout voir dans ce dépôt

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

Informations de source

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Agent Evaluator

Mechanism

Output

Examples

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Agent Evaluator

Mechanism

Output

Examples

Plus depuis ce dépôt