my prefect server setup
prefect-metrics.waow.tech
python
orchestration
add /api/agents/discovery-pool endpoint — recently-liked authors as a generic agent signal
new generic endpoint exposing authors of operator-liked posts (last N days,
default 7) with sample posts. consumers (any agent) can use this as a
high-signal pool of attention; per-consumer filtering happens client-side.
- web/src/lib/server/discovery.ts: loadDiscoveryPool() queries raw_liked_posts,
groups by author, returns top-N with sample posts (default top 30, 3 samples each)
- web/src/routes/api/agents/discovery-pool/+server.ts: GET endpoint with
optional ?window_days, ?max_authors, ?samples_per_author params
- web/src/lib/types.ts: DiscoveryPoolEntry / DiscoveryPoolPost types
not phi-specific by design — any consumer agent can use it. coupling kept
at the JSON schema, not at duckdb columns or per-consumer filtering logic.
Co-Authored-By: Claude Opus 4 (1M context) <noreply@anthropic.com>
+132
+88
web/src/lib/server/discovery.ts
+88
web/src/lib/server/discovery.ts
···
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import { query } from '$lib/server/db';
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import type { DiscoveryPoolEntry, DiscoveryPoolPost } from '$lib/types';
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/** A single liked-post row as it comes back from the DB. */
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interface LikedRow {
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author_handle: string;
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author_did: string;
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subject_uri: string;
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text: string;
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liked_at: string;
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}
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/**
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* Discovery pool: authors whose posts the operator has liked recently,
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* grouped with up to N sample posts each. Generic — not specific to
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* any one consumer agent.
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*
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* @param windowDays how many days back to look at likes
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* @param maxAuthors cap on distinct authors returned
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* @param samplesPerAuthor cap on sample posts per author
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*/
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export async function loadDiscoveryPool(
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windowDays = 7,
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maxAuthors = 30,
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samplesPerAuthor = 3
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): Promise<DiscoveryPoolEntry[]> {
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const rows = await query<LikedRow>(`
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SELECT author_handle, author_did, subject_uri, text, liked_at
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FROM raw_liked_posts
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WHERE liked_at >= (now() - INTERVAL '${windowDays} days')::VARCHAR
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AND author_handle != ''
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ORDER BY liked_at DESC
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`);
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const byAuthor = new Map<string, { did: string; posts: DiscoveryPoolPost[] }>();
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for (const r of rows) {
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const existing = byAuthor.get(r.author_handle);
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if (existing) {
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if (existing.posts.length < samplesPerAuthor) {
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existing.posts.push({
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uri: r.subject_uri,
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text: r.text ?? '',
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liked_at: r.liked_at
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});
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}
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} else {
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byAuthor.set(r.author_handle, {
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did: r.author_did,
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posts: [
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{
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uri: r.subject_uri,
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text: r.text ?? '',
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liked_at: r.liked_at
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}
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]
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});
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}
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}
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// We need the FULL liked count per author (not just the samples we kept).
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const counts = await query<{ author_handle: string; n: number }>(`
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SELECT author_handle, count(*)::INT AS n
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FROM raw_liked_posts
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WHERE liked_at >= (now() - INTERVAL '${windowDays} days')::VARCHAR
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AND author_handle != ''
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GROUP BY author_handle
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`);
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const countByHandle = new Map(counts.map((c) => [c.author_handle, c.n]));
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const entries: DiscoveryPoolEntry[] = Array.from(byAuthor.entries()).map(
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([handle, { did, posts }]) => ({
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handle,
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did,
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likes_in_window: countByHandle.get(handle) ?? posts.length,
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last_liked_at: posts[0]?.liked_at ?? '',
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sample_posts: posts
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})
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);
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entries.sort((a, b) => {
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if (b.likes_in_window !== a.likes_in_window) {
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return b.likes_in_window - a.likes_in_window;
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}
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return b.last_liked_at.localeCompare(a.last_liked_at);
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});
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return entries.slice(0, maxAuthors);
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}
+14
web/src/lib/types.ts
+14
web/src/lib/types.ts
···
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with_reactions: number;
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repos: number;
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}
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export interface DiscoveryPoolPost {
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uri: string;
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text: string;
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liked_at: string;
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}
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export interface DiscoveryPoolEntry {
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handle: string;
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did: string;
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likes_in_window: number;
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last_liked_at: string;
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sample_posts: DiscoveryPoolPost[];
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}
+30
web/src/routes/api/agents/discovery-pool/+server.ts
+30
web/src/routes/api/agents/discovery-pool/+server.ts
···
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import { json } from '@sveltejs/kit';
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import { loadDiscoveryPool } from '$lib/server/discovery';
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/**
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* Generic discovery pool: authors whose posts the operator has liked recently.
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* Consumers (any agent) can use this as a high-signal pool of people to
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* pay attention to. Filtering by per-consumer relevance happens client-side.
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*
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* Query params:
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* window_days — how many days back to consider (default 7)
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* max_authors — cap on distinct authors (default 30)
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* samples_per_author — cap on sample posts per author (default 3)
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*/
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export async function GET({ url }) {
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const windowDays = Math.min(
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90,
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Math.max(1, Number(url.searchParams.get('window_days') ?? 7))
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);
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const maxAuthors = Math.min(
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100,
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Math.max(1, Number(url.searchParams.get('max_authors') ?? 30))
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);
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const samplesPerAuthor = Math.min(
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10,
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Math.max(1, Number(url.searchParams.get('samples_per_author') ?? 3))
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);
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const entries = await loadDiscoveryPool(windowDays, maxAuthors, samplesPerAuthor);
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return json(entries);
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}