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meta-loop-orchestrator

@DNYoussef/context-cascade
8
0

Run nested improvement loops with guardrails for iteration planning, validation, and convergence tracking.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name meta-loop-orchestrator
description Run nested improvement loops with guardrails for iteration planning, validation, and convergence tracking.
allowed-tools Read, Write, Edit, Bash, Glob, Grep, Task, TodoWrite
model sonnet
x-version 3.2.0
x-category orchestration
x-vcl-compliance v3.2.0
x-cognitive-frames HON, MOR, COM, CLS, EVD, ASP, SPC

STANDARD OPERATING PROCEDURE

Purpose

Coordinate iterative improvement loops (e.g., RICE, dogfooding, COV) to refine deliverables while preventing endless iteration or confidence drift.

Trigger Conditions

  • Positive: recursive improvement, self-critique loops, A/B iteration planning, convergence checks, dogfooding cycles.
  • Negative: single-pass edits, straightforward prompt cleanups (route to prompt-architect), or new skill weaving (route to skill-forge).

Guardrails

  • Skill-Forge structure-first: keep SKILL.md, examples/, tests/ updated; add resources//references/ or document gaps.
  • Prompt-Architect hygiene: extract HARD/SOFT/INFERRED goals per iteration, define stop criteria, and state ceilings for confidence.
  • Loop safety: set iteration caps, convergence thresholds, and rollback checkpoints; enforce registry-only agents and hook latencies.
  • Adversarial validation: challenge assumptions every loop, run boundary tests, and record evidence plus deltas.
  • MCP tagging: log loops with WHO=meta-loop-orchestrator-{session} and WHY=skill-execution.

Execution Playbook

  1. Intent & target: define improvement goal, metrics, and stop criteria; confirm inferred needs.
  2. Loop design: select frameworks (COV, dogfooding), assign roles, and timebox iterations.
  3. Execution: run iteration, collect evidence, compute delta; keep artifacts versioned.
  4. Adversarial check: probe edge cases, challenge assumptions, and update risk register.
  5. Convergence decision: compare delta to threshold; decide continue, pivot, or stop.
  6. Delivery: summarize iterations, evidence, residual risks, and confidence ceiling.

Output Format

  • Improvement goal, metrics, and stop rule.
  • Iteration log (changes, evidence, deltas).
  • Risk/assumption register and next steps.
  • Confidence: X.XX (ceiling: TYPE Y.YY) - rationale.

Validation Checklist

  • Structure-first assets present or ticketed; examples/tests reflect current best version.
  • Iteration caps and stop rules respected; registry and hooks validated.
  • Adversarial/COV evidence stored with MCP tags; confidence ceiling declared; English-only output.

Completion Definition

Loop concludes when delta falls below threshold or stop rule triggers, evidence is stored, risks are owned, and a ready-to-use version is documented with confidence ceiling.

Confidence: 0.70 (ceiling: inference 0.70) - Meta-loop SOP aligned to skill-forge scaffolding and prompt-architect clarity with explicit convergence controls.