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Agent Orchestration
Skill

/identify-key-failures

Synthesize the common stuck points across failed decomposition plans. Use when the current batch of decomposition plans has failed — whether they failed already at direct proving or only after further attempts.

From plugin
danus
16017 skills20 agents3 MCP
Install
$ npx -y skills add frenzymath/Danus --skill identify-key-failures --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/identify-key-failures

Context preview

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

Synthesize the common stuck points across failed decomposition plans. Use when the current batch of decomposition plans has failed — whether they failed already at direct proving or only after further attempts.

SKILL.md

identify-key-failures.SKILL.md
name: identify-key-failures
description: Synthesize the common stuck points across failed decomposition plans. Use when the current batch of decomposition plans has failed — whether they failed already at direct proving or only after further attempts.

Identify Key Failures

Use this skill to turn many failed attempts into reusable guidance for the next planning round.

Input Contract

Read:

  • the failed decomposition plans
  • direct-proving stuck points
  • existing `failed_paths`
  • relevant `counterexamples` and `toy_examples`

Procedure

1. Gather the reports from all failed plans. If only direct proving has run so far, work directly from the direct-proving failures. 2. List the key stuck points for each plan. 3. Identify common points across those failures:

  • recurring obstructions or counterexamples
  • decomposition patterns that keep breaking
  • search gaps or missing background facts

4. Summarize what the failures suggest for the next generation of decomposition plans. 5. When all current decomposition plans have failed and no pattern is leading anywhere, publish the synthesized `dead_end` (below): the main agent reads it and consults for a fresh missing-mechanism or alternative direction (delivered as `master_guidance`), which the next planning round picks up. 6. Save the synthesized failure knowledge to `failed_paths` so later planning skills can use it. 7. After recording the failure synthesis, return control to `$propose-subgoal-decomposition-plans`.

Output Contract

Publish the failure synthesis to global memory with `gm_add` (kind `dead_end`): `claim` = the common stuck points, `evidence` = the per-plan failures, so siblings skip these paths. Carry these fields:

{
  "record_type": "key_failures_summary",
  "failed_plan_ids": ["..."],
  "plan_failures": [
    {
      "plan_id": "...",
      "stuck_points": ["..."]
    }
  ],
  "common_failures": ["..."],
  "implications_for_next_plans": ["..."]
}

Also note in your local memory (`events`) that a new planning round is needed.

Tools

  • `gm_add` (publish the dead_end synthesis)
  • `gm_search` (gather the failed plans and stuck points across the swarm)

Failure Logging

If the reports are too weak to identify meaningful common failures, note in local memory (`events`) `event_type="key_failures_inconclusive"` and state what information is still missing.

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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