L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Productivité · SKILL.md

agents-and-models

agents-and-models

Configure and inspect tree-of-thoughts TotAgent model adapters, Thought output contracts, and safe model preflight checks.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: agents-and-models description: "Configure and inspect tree-of-thoughts TotAgent model adapters, Thought output contracts, and safe model preflight checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

agents-and-models

Use this sub-skill when a task needs to configure, inspect, or preflight the tree-of-thoughts model-facing agent layer before using search workflows. It covers TotAgent, the Thought schema, OpenAI caller configuration, injected custom or fake models, model-output validation, and failure diagnosis.

Route here for

  • Verifying that TotAgent imports and that the installed dependency set is compatible.
  • Choosing between the default OpenAI function caller and use_openai_caller=False with a custom or fake model runner.
  • Validating that a model returns dict-like text with thought and numeric evaluation keys.
  • Building deterministic fake agents for safe downstream DFS/BFS smoke checks without external services.
  • Diagnosing import, API-key, parser, constructor side-effect, or no-PyTorch warnings around model setup.

Do not handle here

  • DFS/BFS traversal parameters, pruning behavior, breadth limits, or result interpretation. Route those to the search-workflows sub-skill.
  • Stale maintainer scripts, packaging release automation, or project-wide cleanup. Route those to the root troubleshooting or maintainer references.

Runtime references

  1. Read references/api-reference.md for verified signatures, install/import expectations, environment setup, model-output contracts, and safe fake/custom model examples.
  2. Read references/troubleshooting.md for concrete symptoms, causes, commands, and expected recovery signals.
  3. Use scripts/check_model_contract.py to validate sample model output before passing it into TotAgent.run or DFS/BFS wrappers.

Operating guardrails

  • Treat model output as trusted-only or tightly constrained: this package converts text to a dict with Python eval.
  • Prefer deterministic fake/custom runners for CI, smoke tests, and offline checks; the default TotAgent(use_openai_caller=True) requires OPENAI_API_KEY and external OpenAI-compatible service access.
  • Keep API keys out of prompts, logs, sample outputs, and generated files.
  • Validate the model contract before debugging DFS/BFS behavior; traversal code assumes every generated thought is a dict with exact thought and evaluation keys.
Skills enregistrées