Deliverable Self-Loop & Quality Gate

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Refines and validates research deliverables through an iterative diagnose-fix-verify loop to ensure high-quality, evidence-backed output.

This skill acts as a deterministic quality gate for research pipelines, automating the process of polishing drafts into publication-ready documents. By implementing a structured 'diagnose -> fix -> re-check' workflow, it ensures that deliverables like systematic reviews, tutorials, and research snapshots meet rigorous standards. The skill cross-references outputs against internal data artifacts to prevent 'hollow' writing, ensuring every claim is grounded in evidence and every tutorial step is verifiable, making it an essential tool for maintaining high signal-to-noise ratios in technical and academic workflows.

Características Principales

01Evidence-first validation that enforces citations from core paper sets and extraction tables
02Scope-creep prevention by strictly aligning deliverables with locked upstream contracts and plans
03236 GitHub stars
04Automated reporting via DELIVERABLE_SELFLOOP_TODO.md with clear PASS/FAIL status and blockers
05Multi-rubric support for specific document types including Snapshots, Tutorials, and Syntheses
06Iterative self-correction logic that performs up to three loops to reach a PASS state

Casos de Uso

01Hardening a draft research snapshot by replacing generic narration with concrete, cited claims
02Ensuring a technical tutorial remains consistent with an approved module plan and decision log
03Validating a research idea shortlist against diversity, falsifiability, and minimal validation criteria