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Specification Actionability Assessment

Evaluates specification elements for downstream implementability and produces gap analysis with remediation recommendations.

Updated Apr 11, 2026
Search Tags:#quality assurance#remediation#gap analysis#actionability#requirements review#yaml validation#specification assessment
One-click install
npx skills add https://github.com/gozonerd/shadow-ai-assessment --skill specification-actionability-assessment-gozonerd
Or copy as Structured Prompt for Agentโ–ผ
Please help me install this Agent Skill.
Skill: Specification Actionability Assessment
Source: https://github.com/gozonerd/shadow-ai-assessment/tree/main/.claude/skills/dmis/task_type/TT-17_Specification_Actionability_Assessment
Command: npx skills add https://github.com/gozonerd/shadow-ai-assessment --skill specification-actionability-assessment-gozonerd

SYSTEM DOCUMENTATION & REQUIREMENTS

๐Ÿ’ก This Skill requires pyyaml, and includes scripts (resource) and references (resource) components.

What problem does it solve? Synthesis outputs often contain recommendations and specifications that look complete but lack the concrete details downstream teams need to act. This Skill assesses each specification element for practical implementability, flags underspecified items, and drafts the missing language before execution stalls. ## Core Features & Use Cases - Actionability Scoring: Classifies each specification element as Fully Specified, Partially Specified, or Underspecified based on success criteria, preconditions, resources, and decision rules. - Gap Analysis & Risk Assessment: Identifies missing details, evaluates implementation risks from ambiguity, and distinguishes critical blockers from enhancement opportunities. - Use Case Validation & Remediation: Walks through how downstream consumers would execute each specification and drafts specific language additions, with output validated against a YAML schema via the included Python validator. - Use Case: A synthesis pipeline produces a recommendation like "implement automated data quality checks." This Skill flags it as underspecified, lists the missing validation rules and workflows, and generates a Critical-priority remediation plan. ## Quick Start Assess the actionability of the specifications in this synthesis output and produce a TT-17 compliant gap analysis with remediation recommendations.

Frequently Asked Questions about Specification Actionability Assessment

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I assess whether a specification is actionable for downstream teams?โ–ผ

Evaluate each specification element against five criteria: success criteria, preconditions, resource requirements, decision rules, and responsible parties. Score each as Fully Specified, Partially Specified, or Underspecified, then document gaps and remediation language.

How to validate TT-17 actionability assessment output?โ–ผ

Run the included validate_output.py script against your YAML output file. It checks required sections (specifications_evaluated, gap_analysis, use_case_validation, remediation) and verifies actionability scores and priority values against allowed enumerations.

What output format does the actionability assessment produce?โ–ผ

Output is YAML conforming to the schema in references/output_schema.yaml. It includes specifications_evaluated, actionability scoring, gap_analysis with missing details and impact, use_case_validation with consumer profiles, and prioritized remediation items.

When should a specification be flagged as a critical blocker?โ–ผ

Flag a specification as a critical blocker when downstream consumers cannot execute it without substantial additional detail, such as missing validation rules, undefined workflows, or unknown resource requirements. These items block synthesis from proceeding until expanded or deferred.

What are common mistakes when assessing specification actionability?โ–ผ

Common anti-patterns include assuming downstream consumers have expert context, marking theoretically complete specs as actionable, ignoring edge-case contingencies, and demanding detail beyond reasonable specification scope. Assess against actual consumer capabilities instead.

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