f153f969a0
Add /ultraresearch-local for structured research combining local codebase analysis with external knowledge via parallel agent swarms. Produces research briefs with triangulation, confidence ratings, and source quality assessment. New command: /ultraresearch-local with modes --quick, --local, --external, --fg. New agents: research-orchestrator (opus), docs-researcher, community-researcher, security-researcher, contrarian-researcher, gemini-bridge (all sonnet). New template: research-brief-template.md. Integration: --research flag in /ultraplan-local accepts pre-built research briefs (up to 3), enriches the interview and exploration phases. Planning orchestrator cross-references brief findings during synthesis. Design principle: Context Engineering — right information to right agent at right time. Research briefs are structured artifacts in the pipeline: ultraresearch → brief → ultraplan --research → plan → ultraexecute. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
58 lines
1.8 KiB
JavaScript
58 lines
1.8 KiB
JavaScript
// distribution-stats.mjs — Statistical divergence utilities for behavioral drift detection.
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// Zero external dependencies. <50 lines.
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//
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// Jensen-Shannon divergence measures how different two probability distributions are.
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// Used by post-session-guard.mjs to detect tool distribution shifts within a session.
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//
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// OWASP: ASI01 (Excessive Agency — behavioral pattern changes may indicate hijacking)
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/**
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* Kullback-Leibler divergence KL(P || Q).
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* @param {Map<string, number>} P
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* @param {Map<string, number>} Q
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* @returns {number}
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*/
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function klDivergence(P, Q) {
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let kl = 0;
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for (const [key, p] of P) {
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if (p === 0) continue;
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const q = Q.get(key) || 0;
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if (q === 0) return Infinity;
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kl += p * Math.log2(p / q);
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}
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return kl;
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}
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/**
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* Jensen-Shannon divergence. 0 = identical, 1 = fully disjoint (log2 basis).
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* Always finite, symmetric: JSD(P,Q) = JSD(Q,P).
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* @param {Map<string, number>} P - Normalized probability distribution
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* @param {Map<string, number>} Q - Normalized probability distribution
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* @returns {number}
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*/
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export function jensenShannonDivergence(P, Q) {
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const allKeys = new Set([...P.keys(), ...Q.keys()]);
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const M = new Map();
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for (const key of allKeys) {
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M.set(key, 0.5 * (P.get(key) || 0) + 0.5 * (Q.get(key) || 0));
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}
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return 0.5 * klDivergence(P, M) + 0.5 * klDivergence(Q, M);
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}
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/**
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* Build normalized probability distribution from category labels.
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* @param {string[]} labels
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* @returns {Map<string, number>} Values sum to 1.0 (empty input → empty map)
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*/
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export function buildDistribution(labels) {
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if (labels.length === 0) return new Map();
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const counts = new Map();
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for (const label of labels) {
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counts.set(label, (counts.get(label) || 0) + 1);
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}
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const dist = new Map();
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for (const [key, count] of counts) {
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dist.set(key, count / labels.length);
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}
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return dist;
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}
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