Reusable analysis-recipe framework with sensory recipes#434
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A package-wide recipe framework (process_improve/recipes.py): frozen RecipeStep / AnalysisRecipe dataclasses, a register_recipe registry with lazy discover_recipes (mirroring discover_tools), a substring cue matcher select_recipe, and a general select_analysis_recipe agent tool that matches across every registered recipe and always returns the full catalogue. The sensory subpackage is the first consumer (sensory/recipes.py): guided workflows for data intake, panel consistency / scale-use correction, and relating attributes to covariates with the genuine/proxy/coincidence separation, plus a parked placeholder for a future visualisation workflow. Each step references the real sensory tools by name.
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| return | ||
| for module in _RECIPE_MODULES: | ||
| _import_recipe_module(module) | ||
| _recipes_discovered = True |
tests/test_recipes.py covers the payload serialisation, the registry (duplicate-key rejection), recipe matching, catalog integrity (every tool a recipe step names is a registered tool), and the select_analysis_recipe tool path. MINOR version bump for the new feature, with CITATION and CHANGELOG in sync.
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What and why
Agents have many tools but no guidance layer: faced with an open-ended request
("I have a panel file, what do I do?", "are my panelists consistent?", "what
drives sweetness?") they have to improvise the order of tool calls. This adds a
small, reusable framework for predefined, step-by-step analysis recipes that an
agent matches to a request and follows deterministically, chaining existing tools
by name.
The framework is package-wide and domain-agnostic so any subpackage can register
its own recipes later; the sensory subpackage is the first consumer.
What changed
process_improve/recipes.py(new, general): frozenRecipeStep/AnalysisRecipedataclasses with a JSONto_payload, aregister_reciperegistry plus a lazy
discover_recipes()(mirrorsdiscover_tools), a simplesubstring cue matcher
select_recipe, andlist_recipes/get_recipe. Asingle general agent tool
select_analysis_recipematches across everyregistered recipe and always returns the full catalogue so the agent can offer
a choice even on no match.
process_improve/sensory/recipes.py(new): four guided workflowsregistered into the catalog: data intake (organise + check), panel consistency
/ scale-use correction (with plain-language interpretation of the MAM beta),
relating attributes to covariates (marginal associations plus the
cross-validated discriminator, with the genuine / proxy / coincidence verdict
and the collinearity caveat), and a parked placeholder for a future
visualisation workflow. Steps reference only the real sensory tools.
tool_spec.discover_tools()now importsprocess_improve.recipesso therecipe-selection tool registers alongside the others.
Tests / verification
New
tests/test_recipes.pycovers the dataclass payload, the registry(duplicate-key rejection), matching, catalog integrity (every tool named by a
recipe step is a registered tool), and the agent-tool path.
ruffandmypyclean. Version bumped with the change.
Generated by Claude Code