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Jason-hub-starJason-hub-star/model-orchestrator/skills/converge-plan/SKILL.md

converge-plan

Skill0 starsChanged 2 months agoMITView on GitHub

Stress-test a costly or hard-to-reverse plan before implementation by freezing a rubric, iterating improvements and adversarial critique, and ending with one crux and the cheapest falsifiable experiment. Use for “converge this plan”, “수렴”, major architecture choices, or risky research bets—not routine edits.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this before implementation, not as a generic review after code exists.

    line 8
  2. Distinctive:

    Stop after two consecutive rounds with no new surviving defect, two rounds with less than 5% improvement, or a hard cap of 6–8 rounds.

    line 14
  3. Distinctive:

    Do not declare convergence without at least one primary-source comparison and one direct check of the real system when those are available.

    line 15
  4. Distinctive:

    Stop reason and remaining uncertainty

    line 23
  5. Distinctive:

    Do not change the rubric mid-run or accept an improvement without trying to disprove it.

    line 25

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Converge Plan

Use this before implementation, not as a generic review after code exists.

Workflow

  1. Freeze a small rubric before round one. Suggested dimensions: feasibility, evidence, gap coverage, differentiation, cost realism, and falsifiability.
  2. For each round: improve the largest weakness, score with evidence, attempt to refute the improvement, then record surviving new defects.
  3. Stop after two consecutive rounds with no new surviving defect, two rounds with less than 5% improvement, or a hard cap of 6–8 rounds.
  4. Do not declare convergence without at least one primary-source comparison and one direct check of the real system when those are available.

Output

  • Frozen rubric and score trajectory
  • Accepted plan changes and rejected alternatives
  • One irreversible crux
  • One cheapest falsifiable experiment
  • Stop reason and remaining uncertainty

Do not change the rubric mid-run or accept an improvement without trying to disprove it.

Next

Turn the converged outcome into goal-contract. If the crux is unresolved, run only the cheapest experiment first.

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