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name: search-workflows description: "Run and adapt tree-of-thoughts DFS/BFS search workflows with deterministic validation." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

search-workflows

Use this sub-skill when a task needs to run, adapt, or debug the tree-of-thoughts DFS or BFS orchestration classes. It covers search parameters, output parsing, autosave behavior, deterministic fake-agent smoke tests, and workflow-specific troubleshooting.

Do not use this sub-skill for model/provider setup details. If the task asks how to configure TotAgent, OpenAI credentials, or custom model callers, route that part to agents-and-models and return here for DFS/BFS orchestration.

Runtime assets

• DFS and BFS workflow recipes — constructors, safe recipes, output schemas, validation, autosave notes, and custom/fake agent patterns.
• Workflow troubleshooting — empty/pruned outputs, evaluation ordering surprises, recursion growth, autosave paths, and real-model network/API-key failures.
• Deterministic fake-agent smoke helper — network-free checks for both DFS and BFS.

Fast safe checks

Run from any working directory that has an installed tree-of-thoughts package and dependencies:

python /path/to/search-workflows/scripts/fake_agent_search_smoke.py --mode dfs --max-loops 1 --number-of-agents 2 --no-autosave
python /path/to/search-workflows/scripts/fake_agent_search_smoke.py --mode bfs --max-loops 1 --number-of-agents 2 --breadth-limit 2 --no-autosave

Expected signal: each command exits 0 and prints a JSON summary with the requested mode, generated-thought counts, parsed output keys, and a non-empty fake-agent call count.

Operating rules

1. Import DFS from the root package: from tree_of_thoughts import ToTDFSAgent.
2. Import BFS from its module: from tree_of_thoughts.bfs import BFSWithTotAgent; it is not exported by the package root.
3. Give search agents an object with .run(task) -> {"thought": str, "evaluation": float}. The bundled fake script is the safest template.
4. Treat .run(...) results as JSON strings for both DFS and BFS; parse with json.loads before validating fields.
5. Keep number_of_agents, max_loops, and BFS breadth_limit small during smoke checks because both workflows can multiply calls quickly; start with --max-loops 1 --number-of-agents 2.