Notre avis
Permet de poser une question à un autre modèle d'IA (OpenAI, Anthropic, Google) pour obtenir un regard extérieur sur un problème complexe.
Points forts
- Offre une perspective différente et non biaisée d'un autre modèle
- Prend en charge plusieurs modèles avec raisonnement avancé (GPT-5.2, Claude Opus 4.6, Gemini 3.1 Pro)
- Encadre l'utilisateur pour formuler une question précise et contextuelle
- Commande unique sans suivi, idéale pour débloquer une situation
Limites
- Nécessite que l'utilisateur fournisse tout le contexte dans un seul message (pas de suivi)
- Dépend de clés API externes qui doivent être configurées au préalable
- Coût d'appel potentiellement élevé pour les modèles les plus avancés
Quand vous êtes bloqué sur un problème difficile, que vous tournez en rond, ou que vous soupçonnez qu'il existe un angle que vous n'avez pas envisagé.
Pour des tâches routinières que vous pouvez traiter directement, ou si vous pouvez résoudre le problème avec les ressources locales.
Analyse de sécurité
PrudenceThe skill instructs the agent to execute a Python script via Bash to query external AI models. While it does not contain obfuscation, destructive commands, or exfiltration, the use of network calls and passing user-provided strings as shell arguments introduces moderate risk. Proper quoting is used, but caution is warranted.
- •Uses Bash to run arbitrary Python scripts with uv run, which involves network calls to external AI APIs.
- •User-supplied questions are passed as command-line arguments; special characters could cause injection if not properly handled by the script, though quoting mitigates this.
- •Instructs agent to echo part of the API key for verification, which could be displayed in logs.
Exemples
Here is my auth middleware code: [Paste code]. Users with expired tokens get a 500 instead of 401. I have verified the token validation logic is correct and the error handler is registered. The 500 comes from this stack trace: [Paste trace]. What could cause the error handler to be bypassed?I'm designing a microservices system for a video processing pipeline. The current design uses a message queue to distribute tasks to workers, but I'm concerned about latency spikes when a large video arrives. Here are the details: [Paste context]. What alternative architecture or batching strategy would you recommend? Give me pros and cons.name: discussion_partners description: > Ask a question to another AI model (OpenAI, Anthropic, or Google) with extended thinking enabled. Use when you are stuck on a difficult problem, suspect you are spinning your wheels, or sense there is an angle you have not considered. Do NOT use for routine tasks you can handle directly. allowed-tools: Bash(uv run *)
Query another AI model for an outside perspective on a difficult problem. One message out, one message back — make it count.
Recommended Models
Always use the script default (openai:gpt-5.2) unless you have a specific reason to change. Do NOT override with older models like o3 — they are expensive and superseded.
| Model | When to use | API key needed |
|-------|-------------|----------------|
| openai:gpt-5.2 (default) | Primary partner. xhigh thinking, exceptional detail | OPENAI_API_KEY |
| google-gla:gemini-3.1-pro-preview | Second opinion, brilliantly intelligent reasoning | GOOGLE_API_KEY |
| anthropic:claude-opus-4-6 | Third perspective, different reasoning style | ANTHROPIC_API_KEY |
Before calling, verify the required API key is set: echo $OPENAI_API_KEY | head -c 8 (should show sk-...).
Framing Your Question
You get one message out and one message back. There is no follow-up. Your partner has ZERO context about what you're working on — they see only what you send. The context window is finite and expensive, so treat it like a skill prompt: include everything needed, nothing that isn't.
Your partner needs all context to answer your question. They cannot read your files, see your conversation, or infer your situation. If you don't include it, they don't know it. But context is limited, so be surgical:
- Include all relevant context: code, errors, constraints, what you tried. Use
mental_modelsto structure your thinking before asking. - State what you're stuck on: "I tried A, B, C and none work because D" — not just "help me with X"
- Ask a specific question and set the frame: what kind of answer you need (diagnosis, alternative approach, code review).
- Never include secrets: API keys, credentials, tokens, or private repo URLs. Your question goes to an external API.
Bad: "How do I fix this auth bug?" Good: "Here is my auth middleware [code]. Users with expired tokens get a 500 instead of 401. I have verified the token validation logic is correct and the error handler is registered. The 500 comes from [stack trace]. What could cause the error handler to be bypassed?"
Usage
uv run --directory SKILL_DIR python scripts/ask_model.py -m <model> "Your detailed question with full context"
Where SKILL_DIR is the directory containing this skill. The -m flag takes a full pydantic-ai model string — the provider prefix determines which API key and thinking settings to use.
Models
The default is openai:gpt-5.2. Thinking effort is automatically set to maximum for each provider.
# GPT-5.2 with xhigh reasoning (default — just omit -m)
uv run --directory SKILL_DIR python scripts/ask_model.py "question"
# Claude Opus 4.6 with adaptive thinking at max effort
uv run --directory SKILL_DIR python scripts/ask_model.py -m anthropic:claude-opus-4-6 "question"
# Gemini 3.1 Pro with thinking enabled
uv run --directory SKILL_DIR python scripts/ask_model.py -m google-gla:gemini-3.1-pro-preview "question"
# Codex models (via OpenAI Responses API)
uv run --directory SKILL_DIR python scripts/ask_model.py -m openai-responses:gpt-5-codex "question"
uv run --directory SKILL_DIR python scripts/ask_model.py -m openai-responses:codex-mini-latest "question"
API Key Setup
Add keys to your shell profile (~/.zshrc or ~/.bashrc):
export OPENAI_API_KEY="your-key" # Required for openai: and openai-responses: models
export ANTHROPIC_API_KEY="your-key" # Required for anthropic: models
export GOOGLE_API_KEY="your-key" # Required for google-gla: models
The script checks for the key before calling the API. If missing, it tells you which variable to set. If the key exists but the call fails, common errors:
- insufficient_quota (429): Billing issue — add credits at the provider's dashboard.
- invalid_api_key (401): Wrong key — check you exported the correct one and ran
source ~/.zshrc. - rate_limit (429): Too many requests — wait and retry.
Options
--model/-m: Full pydantic-ai model string (default:openai:gpt-5.2)--system/-s: Optional system prompt override--list-models/-l: List known model names, optionally filtered by prefix (e.g.-l openai,-l anthropic). Codex models appear underopenai:but must be called withopenai-responses:prefix.
Multiple Calls
Each call gets zero context from previous calls. For follow-ups, include the prior exchange: "I asked X, you answered Y, help me understand Z."
Expert Next.js App Router
Developpement
Un skill qui transforme Claude en expert Next.js App Router.
Générateur de README
Developpement
Crée des README.md professionnels et complets pour vos projets.
Rédacteur de Documentation API
Developpement
Génère de la documentation API complète au format OpenAPI/Swagger.