matching-existing-controls

Scores requirement-to-control similarity to decide reuse, review, or new control creation.

1|Updated Jun 11, 2026
One-click install
npx skills add https://github.com/fianulabs/fianu-skills --skill matching-existing-controls-fianulabs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: matching-existing-controls
Source: https://github.com/fianulabs/fianu-skills/tree/main/skills/matching-existing-controls
Command: npx skills add https://github.com/fianulabs/fianu-skills --skill matching-existing-controls-fianulabs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Compliance teams converting framework requirements into Fianu controls risk creating duplicate controls that fragment evidence collection. This Skill determines whether an existing published control already satisfies a new requirement before a new one is designed. ## Core Features & Use Cases - Weighted Similarity Scoring: Computes a 0.0–1.0 similarity score across four axes: name similarity (0.35), description overlap (0.30), category alignment (0.20), and plugin relevance (0.15). - Threshold-Based Actions: Maps scores to decisions — reuse at 0.80 or above, flag for human review between 0.50 and 0.79, and proceed to new control design below 0.50. - Use Case: When converting a SOC 2 requirement like CC-7.1 into controls, score it against all active published controls fetched via the Fianu API to avoid duplicating an existing "Pull Request Review" control. ## Quick Start Check whether the requirement "CC-7.1: manage changes to infrastructure through documented review" is already covered by an existing Fianu control.

Frequently Asked Questions about matching-existing-controls

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

FAQPage Schema
How do I check if a compliance requirement already has an existing control?▼

Fetch all active published controls via GET /controls?status=active&state=published, then compute a weighted similarity score for each candidate against the requirement. Scores of 0.80 or above indicate the existing control can be reused.

What similarity threshold should I use for control matching?▼

Use 0.80 as the threshold for automatic reuse and 0.50 as the floor for possible matches. Scores between 0.50 and 0.79 should be flagged for human review rather than silently reused, and anything below 0.50 means designing a new control.

How is control similarity scored across name and description?▼

Name similarity uses token-set Jaccard overlap weighted at 0.35, while description overlap uses keyword extraction and shared concept matching weighted at 0.30. Category alignment contributes 0.20 and plugin relevance 0.15 to the total score.

When should a possible control match be flagged for human review?▼

Flag matches scoring between 0.50 and 0.79 for human review because the similarity is plausible but not certain enough for automatic reuse. For example, a score of 0.70 indicates overlapping concepts but insufficient confidence to map the requirement silently.

What are the limitations of lexical similarity for control matching?▼

Lexical methods like token-set Jaccard can miss semantically equivalent controls phrased differently and may over-score controls sharing generic compliance vocabulary. The 0.50–0.79 review band exists precisely to catch these ambiguous cases.