Contextual Plugin Intelligence

Analyze a project's technology stack and recommend plugins using machine learning techniques: $ARGUMENTS

Overview

The intelligence module provides three core capabilities:

• Project Fingerprinting — Scans a project directory to extract a feature vector covering frameworks, languages, infrastructure, and architectural patterns.

• Association Rule Mining (Apriori) — Discovers which features commonly co-occur across projects, then identifies capability gaps in the current project.

• Plugin Recommendations — Ranks available plugins by cosine similarity to the project fingerprint, weighted by gap-filling potential.

Architecture

ProjectFingerprinter | |-- detectFrameworks() -> package.json, pyproject.toml, go.mod, Cargo.toml |-- detectLanguages() -> file extension distribution (recursive walk) |-- detectInfrastructure() -> config files (Dockerfile, Chart.yaml, .github/workflows, etc.) |-- detectPatterns() -> monorepo, event-driven, api-gateway, microservices, serverless |-- findGaps() -> uses AprioriMiner rules to identify missing capabilities | v ProjectFingerprint { frameworks, languages, infrastructure, patterns, missing } | v RecommendationEngine |-- buildVocabulary() -> union of all feature terms |-- toBinaryVector() -> project/plugin features -> [0,1,0,1,...] vectors |-- cosineSimilarity() -> dot(A,B) / (||A|| * ||B||) |-- computeGapCoverage() -> weighted gap fill score |-- recommend() -> ranked PluginRecommendation[] | v RecommendationReport { projectSummary, recommendations, gaps, scanDate }

Key Algorithms

Apriori Algorithm

The Apriori algorithm mines association rules from a dataset of project profiles. It works in two phases:

Phase 1: Find Frequent Itemsets

L1 = { items appearing in >= minSupport fraction of transactions } k = 2 while L(k-1) is non-empty: Candidates = apriori-gen(L(k-1)) // join + prune Count each candidate's support across all transactions L(k) = candidates with support >= minSupport k++

The Apriori principle (anti-monotone property) states: if {A,B} is infrequent, no superset {A,B,C,...} can be frequent. This allows aggressive pruning of the candidate space.

Phase 2: Generate Rules

For each frequent itemset S where |S| >= 2:

For each item B in S: A = S \ {B} confidence = support(S) / support(A) lift = confidence / support({B}) if confidence >= minConfidence: emit rule A => {B}

Cosine Similarity

Projects and plugins are both represented as binary vectors over a shared vocabulary of feature terms:

vocabulary = sorted union of all terms project_vector[i] = 1 if vocabulary[i] in project_features else 0 plugin_vector[i] = 1 if vocabulary[i] in plugin_capabilities else 0

similarity = dot(project, plugin) / (norm(project) * norm(plugin))

Final Scoring

relevance = 0.6 * cosine_similarity + 0.4 * gap_coverage gap_coverage = sum(confidence[gap] for filled gaps) / sum(confidence[gap] for all gaps)

File Structure

plugins/marketplace-pro/ src/intelligence/ types.ts — All TypeScript interfaces fingerprint.ts — ProjectFingerprinter, AprioriMiner, RecommendationEngine config/ project-profiles.json — Training dataset (~22 project profiles) commands/ recommend.md — /mp:recommend slash command skills/intelligence/ SKILL.md — This file

Usage Examples

Scan Current Project

import { ProjectFingerprinter } from './src/intelligence/fingerprint.js';

const fp = new ProjectFingerprinter('/path/to/project'); const fingerprint = await fp.scan();

console.log('Frameworks:', fingerprint.frameworks); console.log('Languages:', fingerprint.languages); console.log('Infrastructure:', fingerprint.infrastructure); console.log('Patterns:', fingerprint.patterns); console.log('Missing capabilities:', fingerprint.missing);

Mine Association Rules

import { AprioriMiner } from './src/intelligence/fingerprint.js';

const profiles = [ { features: ['typescript', 'react', 'nextjs', 'eslint', 'jest', 'ci-cd'] }, { features: ['typescript', 'nodejs', 'express', 'docker', 'kubernetes', 'helm', 'ci-cd'] }, { features: ['python', 'fastapi', 'docker', 'terraform', 'aws', 'monitoring'] }, // ...more profiles ];

const miner = new AprioriMiner(profiles, 0.3, 0.6); const rules = miner.mineRules();

for (const rule of rules) { console.log( {${rule.antecedent.join(', ')}} => {${rule.consequent.join(', ')}}, support=${rule.support} confidence=${rule.confidence} lift=${rule.lift} ); } // Example output: // {kubernetes, helm} => {ci-cd} support=0.318 confidence=0.875 lift=1.05 // {docker, kubernetes} => {monitoring} support=0.364 confidence=0.8 lift=1.12

Get Plugin Recommendations

import { RecommendationEngine } from './src/intelligence/fingerprint.js'; import type { PluginCapability } from './src/intelligence/types.js';

const plugins: PluginCapability[] = [ { name: 'ci-pipeline-pro', description: 'CI/CD pipeline generator', capabilities: ['ci-cd', 'testing', 'deployment'], targetInfrastructure: ['docker', 'kubernetes'], }, { name: 'monitoring-stack', description: 'Observability setup with Prometheus + Grafana', capabilities: ['monitoring', 'alerting', 'dashboards'], targetInfrastructure: ['kubernetes', 'docker'], }, ];

const engine = new RecommendationEngine(); const report = engine.recommend(fingerprint, plugins);

for (const rec of report.recommendations) { console.log(${rec.pluginName}: relevance=${rec.relevance}); console.log( Reason: ${rec.reason}); console.log( Gaps filled: ${rec.gapsFilled.join(', ')}); }

Full Analysis (Convenience Function)

import { analyzeProject } from './src/intelligence/fingerprint.js';

const report = await analyzeProject('/path/to/project', availablePlugins); console.log(JSON.stringify(report, null, 2));

Configuration

Support and Confidence Thresholds

The AprioriMiner accepts two thresholds:

Parameter Default Description

minSupport

0.3 Minimum fraction of profiles containing an itemset to be "frequent"

minConfidence

0.6 Minimum conditional probability for a rule to be emitted

Lower support finds more rules but may include noise. Higher confidence produces more reliable gap predictions.

Gap Filtering

Only gaps with confidence >= 0.6 are surfaced to users. This threshold is hardcoded in ProjectFingerprinter.findGaps() to avoid noisy suggestions.

Training Data

The Apriori miner learns from config/project-profiles.json . Add new profiles to improve rule quality:

{ "label": "my-custom-stack", "features": ["typescript", "react", "nextjs", "docker", "ci-cd", "monitoring"] }

More diverse profiles = better association rules = more accurate gap detection.