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Agent Orchestration
Skill

/propose-subgoal-decomposition-plans

Propose multiple subgoal decomposition plans for the current theorem using the information already gathered. Use when enough information has been collected from examples, counterexamples, search results, and previous failures to break the problem into several materially

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danus
16017 skills20 agents3 MCP
Install
$ npx -y skills add frenzymath/Danus --skill propose-subgoal-decomposition-plans --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/propose-subgoal-decomposition-plans

Context preview

The summary Claude sees to decide when to auto-load this skill.

Propose multiple subgoal decomposition plans for the current theorem using the information already gathered. Use when enough information has been collected from examples, counterexamples, search results, and previous failures to break the problem into several materially

SKILL.md

propose-subgoal-decomposition-plans.SKILL.md
name: propose-subgoal-decomposition-plans
description: Propose multiple subgoal decomposition plans for the current theorem using the information already gathered. Use when enough information has been collected from examples, counterexamples, search results, and previous failures to break the problem into several materially different plans.

Propose Subgoal Decomposition Plans

Use this skill when the agent has enough context to propose several viable decomposition plans.

Input Contract

Read:

  • the current target theorem or branch goal
  • relevant `immediate_conclusions`, `toy_examples`, and `counterexamples`
  • relevant `failed_paths` and `branch_states`
  • recent search results and useful references from `events`

Procedure

1. Gather the current information that materially constrains the problem: useful examples, failed claims, known obstructions, and relevant search results. 2. Propose materially different decomposition plans. 3. For each plan, state:

  • the main idea of the plan
  • the ordered subgoals
  • why this plan is plausible given the current information
  • which earlier failures or counterexamples it tries to avoid

4. Hand each plan to `$direct-proving` for a quick screening pass.

Output Contract

Publish one plan per decomposition to global memory with `gm_add` (kind `plan`, a judgment — `verifiable=false`): `claim` = the plan's goal + summary, `evidence` = its motivation, plus these fields:

{
  "plan_id": "...",
  "record_type": "decomposition_plan",
  "goal": "...",
  "plan_summary": "...",
  "subgoals": ["..."],
  "motivation": ["..."],
  "uses_information_from": {
    "examples": ["..."],
    "counterexamples": ["..."],
    "key_failures": ["..."],
    "search_results": ["..."]
  },
  "status": "proposed|screening|screened|selected|failed|solved",
  "branch_id": "optional"
}

Also note the new plan set in your local memory (`events`).

Tools

  • `gm_add` (publish the plan findings)
  • `gm_search` (recall the examples/counterexamples/dead-ends the plans build on)
  • `search_arxiv_theorems`

Failure Logging

If the agent cannot yet propose meaningful decomposition plans, append an `events` record with:

  • `event_type="decomposition_plans_not_ready"`
  • the missing information
  • the blockers that prevent proposing plans
Read more
Ships withdanus

Danus orchestrates mathematical reasoning agents with fact-graph memory. A main agent (Claude Code) steers a swarm of autonomous codex workers that prove; a cold-start verifier is the sole authority on correctness: a result becomes real only once it passes.

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