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agents-and-models

agents-and-models

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

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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.
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