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https://github.com/vitorpamplona/amethyst.git
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feat(amethyst): getFeedDigest verb — feed-summary surface for the LLM
New verb: getFeedDigest(hoursBack, maxNotes).
Use when the user asks "summarize my Nostr feed", "give me a digest
of what my follows posted today", "recap Nostr", or any other
summary / digest / recap intent.
Returns a structured snapshot for AI summary instead of a raw note
list: total note count, unique author count, top hashtags (≤10) and
top mentioned users (≤10) — with display names resolved from the
local kind:0 cache — alongside the trimmed note body. The LLM uses
the aggregate signals to write a one-paragraph "the conversation
focused on X, with N people posting about Y" instead of having to
re-derive frequencies from a raw list.
Implementation:
* Shared core extracted into fetchFollowFeed(account, since, limit)
so getRecentFromFollows and getFeedDigest don't duplicate the
drain logic.
* Over-fetches by 3× the visible cap so stats are computed over a
larger sample than the LLM sees, capped at 500 events for bounded
on-device work.
* Hashtag bucketing: lowercases + strips leading #, so #Bitcoin
and #bitcoin collapse.
* Mention bucketing: skips self-mentions (some clients tag the
author themself, not useful for the digest).
New @AppFunctionSerializable result types:
* HashtagFrequency, MentionFrequency — count + identifier.
* FeedDigestResult — windowHours, totalNoteCount, uniqueAuthorCount,
topHashtags, topMentions, notes.
Total verb count: 22. app_metadata.xml updated so Gemini's tool
picker can pitch the summary surface specifically.
Known scope: currently returns kind:1 from the user's kind:3 follow
list — does NOT match the in-app home feed exactly. The home feed
includes reposts, long-form, polls, comments, etc., respects the
user's currently selected NIP-51 list, and filters muted users.
Aligning the digest to the home feed (via HomeNewThreadFeedFilter
against LocalCache) is a documented follow-up.
This commit is contained in:
Claude
+198
-15
@@ -175,35 +175,146 @@ class AmethystAppFunctions {
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): SearchNotesResult {
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val cappedLimit = limit.coerceIn(1, 200)
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val account = Amethyst.instance.sessionManager.loggedInAccount() ?: return SearchNotesResult.empty()
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val client = Amethyst.instance.client
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val events = fetchFollowFeed(account = account, sinceSecs = null, limit = cappedLimit)
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return SearchNotesResult(matches = events.map { it.toNoteHit() })
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}
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/**
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* Build a structured digest of the user's Nostr feed for an AI
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* summary. Use when the user asks "summarize my Nostr feed",
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* "give me a digest of what my follows posted today", "recap
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* Nostr for me", "what have people been talking about on Nostr",
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* or any "summary / digest / recap of my Nostr timeline" intent.
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*
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* Returns the raw notes plus pre-extracted signals the LLM needs
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* to write a useful summary without re-deriving them: total note
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* count, unique author count, top hashtags in the window, and the
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* most-mentioned users (with display names resolved from the local
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* kind:0 cache). The LLM composes the natural-language summary
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* from these.
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*
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* @param hoursBack window size in hours. Capped to 168 (7 days),
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* default 12.
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* @param maxNotes max notes returned in the body. Capped to 200.
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* Default 60 — big enough for a meaningful summary, small enough
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* to fit comfortably in the LLM's prompt.
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*/
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@AppFunction(isDescribedByKDoc = true)
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suspend fun getFeedDigest(
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appFunctionContext: AppFunctionContext,
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hoursBack: Int = 12,
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maxNotes: Int = 60,
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): FeedDigestResult {
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val cappedHours = hoursBack.coerceIn(1, 24 * 7)
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val cappedMaxNotes = maxNotes.coerceIn(1, 200)
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val account = Amethyst.instance.sessionManager.loggedInAccount() ?: return FeedDigestResult.empty()
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val sinceSecs = TimeUtils.now() - cappedHours.toLong() * 3600L
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// Over-fetch a bit so the stats are computed over a larger
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// sample than the trimmed `notes` list — the LLM gets richer
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// signal without seeing every note. Cap at 500 events to keep
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// the on-device work bounded.
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val events =
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fetchFollowFeed(
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account = account,
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sinceSecs = sinceSecs,
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limit = (cappedMaxNotes * 3).coerceAtMost(500),
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)
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// Hashtag frequencies — case-folded so `#Bitcoin` and
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// `#bitcoin` collapse to one bucket.
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val hashtagCounts = HashMap<String, Int>()
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// Mention frequencies, keyed by mentioned pubkey hex.
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val mentionCounts = HashMap<HexKey, Int>()
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val uniqueAuthors = HashSet<HexKey>()
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for (ev in events) {
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uniqueAuthors.add(ev.pubKey)
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for (tag in ev.tags) {
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if (tag.size < 2) continue
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when (tag[0]) {
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"t" -> {
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val cleaned = tag[1].trim().removePrefix("#").lowercase()
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if (cleaned.isNotEmpty()) {
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hashtagCounts.merge(cleaned, 1, Int::plus)
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}
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}
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"p" -> {
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// Skip self-mentions (the author tags themself
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// in some clients) — not useful for the digest.
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if (tag[1].length == 64 && tag[1] != ev.pubKey) {
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mentionCounts.merge(tag[1], 1, Int::plus)
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}
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}
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}
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}
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}
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val topHashtags =
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hashtagCounts.entries
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.sortedByDescending { it.value }
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.take(TOP_HASHTAGS_LIMIT)
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.map { HashtagFrequency(tag = it.key, noteCount = it.value) }
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val topMentions =
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mentionCounts.entries
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.sortedByDescending { it.value }
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.take(TOP_MENTIONS_LIMIT)
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.map { (pub, count) ->
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MentionFrequency(
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npub = NPub.create(pub),
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pubkeyHex = pub,
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displayName = displayNameOf(pub),
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mentionCount = count,
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)
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}
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return FeedDigestResult(
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windowHours = cappedHours,
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totalNoteCount = events.size,
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uniqueAuthorCount = uniqueAuthors.size,
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topHashtags = topHashtags,
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topMentions = topMentions,
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// Truncate to the caller-requested limit for the body —
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// the LLM has the stats either way and doesn't need every
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// note quoted.
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notes = events.take(cappedMaxNotes).map { it.toNoteHit() },
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)
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}
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/**
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* Shared core of [getRecentFromFollows] and [getFeedDigest]: drain
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* recent kind:1 notes from the active account's follow set,
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* optionally filtered by `since`. Returns empty when there's no
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* account, no follows, or no relays configured.
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*/
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private suspend fun fetchFollowFeed(
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account: com.vitorpamplona.amethyst.model.Account,
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sinceSecs: Long?,
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limit: Int,
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): List<TextNoteEvent> {
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val authors = account.kind3FollowList.flow.value.authors
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if (authors.isEmpty()) return SearchNotesResult.empty()
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if (authors.isEmpty()) return emptyList()
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val relays =
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account.homeRelays.flow.value
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.ifEmpty { DefaultNIP65RelaySet }
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if (relays.isEmpty()) return SearchNotesResult.empty()
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if (relays.isEmpty()) return emptyList()
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val filter =
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Filter(
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kinds = listOf(TextNoteEvent.KIND),
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authors = authors.toList(),
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limit = cappedLimit,
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since = sinceSecs,
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limit = limit,
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)
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val events =
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client.fetchAll(
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return Amethyst.instance.client
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.fetchAll(
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filters = relays.associateWith { listOf(filter) },
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timeoutMs = GEMINI_FETCH_TIMEOUT_MS,
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)
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val hits =
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events
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.mapNotNull { it as? TextNoteEvent }
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.take(cappedLimit)
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.map { it.toNoteHit() }
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return SearchNotesResult(matches = hits)
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).mapNotNull { it as? TextNoteEvent }
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.take(limit)
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}
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/**
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@@ -1641,6 +1752,15 @@ class AmethystAppFunctions {
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* a few seconds; 30s is generous without letting a stuck
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* wallet stall the dispatch indefinitely. */
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private const val NWC_PAYMENT_TIMEOUT_MS = 30_000L
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/** Cap on the number of distinct hashtags surfaced in a feed
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* digest. Picked to fit a one-paragraph summary without
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* noise — the long tail won't help the LLM. */
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private const val TOP_HASHTAGS_LIMIT = 10
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/** Cap on the number of mentioned users surfaced in a feed
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* digest. Same rationale as TOP_HASHTAGS_LIMIT. */
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private const val TOP_MENTIONS_LIMIT = 10
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}
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}
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@@ -1676,6 +1796,69 @@ class SearchProfilesResult(
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}
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}
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/** One hashtag and how many notes in the digest window carried it.
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* Lowercased and stripped of the leading `#`. */
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@AppFunctionSerializable(isDescribedByKDoc = true)
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class HashtagFrequency(
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/** The hashtag value without the leading `#`, lowercased. */
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val tag: String,
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/** Number of notes in the digest window that carried this tag. */
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val noteCount: Int,
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)
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/** One pubkey that was mentioned via `p` tags in the digest window,
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* with display name resolved from the local kind:0 cache when known. */
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@AppFunctionSerializable(isDescribedByKDoc = true)
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class MentionFrequency(
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/** Bech32 npub of the mentioned user. */
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val npub: String,
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/** Hex pubkey of the mentioned user. */
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val pubkeyHex: String,
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/** Best-effort display name from the local kind:0 cache. Null when
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* the user's profile hasn't been seen yet — caller falls back to
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* the npub. */
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val displayName: String?,
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/** Number of notes in the digest window that mention this user. */
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val mentionCount: Int,
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)
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/**
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* Structured snapshot of the active account's Nostr feed for
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* [AmethystAppFunctions.getFeedDigest]. The LLM uses the aggregate
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* signals (counts + top hashtags + top mentions) to write a one- or
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* two-paragraph summary; the raw [notes] list is included for
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* follow-up questions ("which post was about X?").
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*/
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@AppFunctionSerializable(isDescribedByKDoc = true)
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class FeedDigestResult(
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/** Window size in hours actually queried (after capping). */
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val windowHours: Int,
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/** Total notes scanned for stats. May exceed [notes].size when the
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* body was truncated to fit the LLM prompt. */
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val totalNoteCount: Int,
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/** Distinct authors who posted in the window. */
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val uniqueAuthorCount: Int,
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/** Top hashtags by note count — at most 10. */
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val topHashtags: List<HashtagFrequency>,
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/** Most-mentioned users by note count — at most 10. */
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val topMentions: List<MentionFrequency>,
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/** Notes themselves (truncated to the caller's maxNotes). Newest-
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* first; same fields as [NoteHit] returned by the search verbs. */
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val notes: List<NoteHit>,
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) {
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companion object {
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fun empty() =
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FeedDigestResult(
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windowHours = 0,
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totalNoteCount = 0,
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uniqueAuthorCount = 0,
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topHashtags = emptyList(),
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topMentions = emptyList(),
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notes = emptyList(),
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)
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}
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}
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/** Single match in [SearchNotesResult]. */
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@AppFunctionSerializable(isDescribedByKDoc = true)
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class NoteHit(
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@@ -12,5 +12,5 @@
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the @AppFunction surface grows.
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-->
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<AppFunctionAppMetadata xmlns:appfn="http://schemas.android.com/apk/res-auto"
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appfn:description="Amethyst is a Nostr social client. The agent can read: search Nostr profiles, notes, hashtags, and long-form articles; look up any user's profile by npub; read recent posts from the signed-in user's follows or any specific user; read recent direct messages (decrypted); list NIP-57 zaps received and total sats earned in a time window; surface notes that mention or reply to the user; list currently-live audio/video streams; and report basic account info. The agent can also write: publish short text notes, follow or unfollow other users, and send NIP-17 gift-wrapped direct messages — provided the user is signed in with a local key or NIP-46 bunker. NIP-55 external signers (Amber) are read-only from the agent for now; open Amethyst directly to publish."
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appfn:displayDescription="Read and write Nostr through Amethyst" />
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appfn:description="Amethyst is a Nostr social client. The agent can read: search Nostr profiles, notes, hashtags, and long-form articles; look up any user's profile by npub; read recent posts from the signed-in user's follows or any specific user; summarize the feed for a time window (with top hashtags, top mentions, counts pre-extracted for AI digestion); read recent direct messages (decrypted); list NIP-57 zaps received and total sats earned in a time window; surface notes that mention or reply to the user; list currently-live audio/video streams; and report basic account info. The agent can also write: publish short text notes, follow or unfollow other users, send NIP-17 gift-wrapped direct messages, and zap users or specific notes via Lightning (with NWC auto-pay when configured) — provided the user is signed in with a local key or NIP-46 bunker. NIP-55 external signers (Amber) are read-only from the agent for now."
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appfn:displayDescription="Read, write, summarize, and zap Nostr through Amethyst" />
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