How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill decompose-and-or-goalBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
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Original skill document
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
decompose-and-or-goal
Purpose
Recursively decompose a goal into an AND/OR DAG whose leaves are executable or testable subgoals.
Input contract
yaml
required: [top_goal, constraints]
optional: [actor_profile, obstacle_report, existing_subgoals]
constraints: [branch semantics, dependencies, and leaf criteria must be explicit]Procedure
- Split the goal into necessary AND conditions and alternative OR paths.
- Recurse until each leaf has an observable result or executable action.
- Attach actors, dependencies, constraints, and unresolved feasibility to each branch.
- Preserve shared subgoals as DAG nodes rather than duplicating them.
If the AND/OR decomposition is complete enough to test necessity and coverage, consider validate-goal-tree as the next tactic.
Output contract
yaml
produces: [goal_dag, branch_semantics, executable_leaves, dependency_edges, feasibility_questions]
delta_fields: [findings, hypothesis_updates, uncertainties, open_questions]Quality gates
- Every leaf traces to the top goal.
- AND/OR labels and dependency directions are unambiguous.
- Cycles and duplicate shared nodes are flagged.
Failure and counterexamples
Do not split prose into labels without changing testability, and do not treat alternatives as simultaneous requirements.
Provenance map
resolved: north-star-crystallization-and-or-decompose
