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- Channel-2 auto-responder (`src/commands`): event-driven — `CommandService.onRx()` is called by `MeshService` for every decoded rx text (right after the notify gate) and dispatches exact, case-sensitive `#command` matches on `COMMAND_CHANNEL` (env, default 2) through a single-registration-point registry (handlers registered in the constructor via `register(name, fn)`; handler gets `{ text, from?, id? }`, returns a reply string — or an array of lines, sent as one message per line through the same routing — or void = no reply). First command: `#ping` → replies `#pong!`. Replies go through `MeshService.respond(id, reply)`: auto-routed (rx → DM back to the original sender unless the original was a broadcast; tx → its destination), riding the original record's channel with `replyId = id` so the recipient app renders it as a threaded quote — `COMMAND_CHANNEL` is only the trigger/listen gate. Only when the trigger has no packet id does it fall back to legacy sendText routing on `COMMAND_CHANNEL`. A failing handler/respond only logs a warn. DI: provided by `CommandModule`'s factory (injects `MeshService`, attaches itself via `attachCommands()`) — no constructor-level cycle; `forwardRef` was tried and left one side null in NestJS 12's injector. Prefix dispatch (`registerPrefix(name, fn)`) lets one handler own a whole family: the `#rss` subcommands live in `src/rss` (`RssService.handle`, registered as prefix `'rss'` from the CommandModule factory — exact matches still win over prefixes). Subcommands: `#rss help`; `#rss add <url> <name>` (validates URL, fetches with 15 s timeout, requires ≥1 titled item, rejects duplicate names case-insensitively; LLM generates 2-4 lowercase tags from up to 8 latest titles — on LLM failure the source is added with empty tags and the reply says so); `#rss remove <name>`; `#rss tags` (all distinct tags); `#rss sources`; `#rss get <name|#tag>` (LLM headline summary as 3-5 SEPARATE mesh messages: prompt asks for exactly 3-5 lines, one item per line, `<short header>: <one or two short sentences>` with ≤20-char header, no intro/outro/numbering; reply parsed into items — drop empty/separator-less lines, cap at 5, fewer than 3 → send what we got (no retry), zero → no message; each item sent via its own respond() threaded to the trigger packet id, per-line backstop `fitOneLine()` hard-cuts >185-char lines at a word boundary near 180 so every item fits one packet; maxTokens 2500 is headroom because the LLM server's reasoning tokens count against it — see rss.service.ts comment; `#tag` collects items across ALL sources carrying that tag — one dead feed does not sink the summary). Storage: `rss_sources(name TEXT PRIMARY KEY, url, tags, created_at)` table in the EXISTING MESH_DB_PATH file via a second node:sqlite connection (names matched case-insensitively); feed parsing via `fast-xml-parser` (RSS 2.0 + Atom; malformed/no-items → "not a valid feed"); LLM via injected `LlmService.chat()` with explicit maxTokens (50 tags / 2500 summary headroom).
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- Channel-2 auto-responder (`src/commands`): event-driven — `CommandService.onRx()` is called by `MeshService` for every decoded rx text (right after the notify gate) and dispatches exact, case-sensitive `#command` matches on `COMMAND_CHANNEL` (env, default 2) through a single-registration-point registry (handlers registered in the constructor via `register(name, fn)`; handler gets `{ text, from?, id? }`, returns a reply string — or an array of lines, sent as one message per line through the same routing — or void = no reply). First command: `#ping` → replies `#pong!`. Replies go through `MeshService.respond(id, reply)`: auto-routed (rx → DM back to the original sender unless the original was a broadcast; tx → its destination), riding the original record's channel with `replyId = id` so the recipient app renders it as a threaded quote — `COMMAND_CHANNEL` is only the trigger/listen gate. Only when the trigger has no packet id does it fall back to legacy sendText routing on `COMMAND_CHANNEL`. A failing handler/respond only logs a warn. DI: provided by `CommandModule`'s factory (injects `MeshService`, attaches itself via `attachCommands()`) — no constructor-level cycle; `forwardRef` was tried and left one side null in NestJS 12's injector. Prefix dispatch (`registerPrefix(name, fn)`) lets one handler own a whole family: the `#rss` subcommands live in `src/rss` (`RssService.handle`, registered as prefix `'rss'` from the CommandModule factory — exact matches still win over prefixes). Subcommands: `#rss help`; `#rss add <url> <name>` (validates URL, fetches with 15 s timeout, requires ≥1 titled item, rejects duplicate names case-insensitively; LLM generates 2-4 lowercase tags from up to 8 latest titles — on LLM failure the source is added with empty tags and the reply says so); `#rss remove <name>`; `#rss tags` (all distinct tags); `#rss sources`; `#rss get <name|#tag>` (LLM headline summary as 3-5 SEPARATE mesh messages: prompt asks for exactly 3-5 lines, one item per line, `<short header>: <one or two short sentences>` with ≤20-char header, no intro/outro/numbering; reply parsed into items — drop empty/separator-less lines, cap at 5, fewer than 3 → send what we got (no retry), zero → no message; each item sent via its own respond() threaded to the trigger packet id, per-line backstop `fitOneLine()` hard-cuts >185-char lines at a word boundary near 180 so every item fits one packet; maxTokens 2500 is headroom because the LLM server's reasoning tokens count against it — see rss.service.ts comment; `#tag` collects items across ALL sources carrying that tag — one dead feed does not sink the summary). Storage: `rss_sources(name TEXT PRIMARY KEY, url, tags, created_at)` table in the EXISTING MESH_DB_PATH file via a second node:sqlite connection (names matched case-insensitively); feed parsing via `fast-xml-parser` (RSS 2.0 + Atom; malformed/no-items → "not a valid feed"); LLM via injected `LlmService.chat()` with explicit maxTokens (50 tags / 2500 summary headroom).
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- LLM client (`src/llm`): `LlmService` wraps the local OpenAI-compatible server (default `http://192.168.1.70:1111/v1`, no auth) with native fetch — plain feature module, no Mesh/Schedule dependency. Methods: `listModels()` → `data[].id`; `chat(messages, { maxTokens?, temperature? })` → `choices[0].message.content` (non-2xx throws with status + body snippet); `summarize(text)` = chat() with system prompt "Summarize the following text concisely." Env: `LLM_API_BASE`, `LLM_MODEL` (default `qwen3.8-27b`), `LLM_TIMEOUT_MS` (default 60000, via `AbortSignal.timeout`).
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- LLM client (`src/llm`): `LlmService` wraps the local OpenAI-compatible server (default `http://192.168.1.70:1111/v1`, no auth) with native fetch — plain feature module, no Mesh/Schedule dependency. Methods: `listModels()` → `data[].id`; `chat(messages, { maxTokens?, temperature? })` → `choices[0].message.content` (non-2xx throws with status + body snippet); `summarize(text)` = chat() with system prompt "Summarize the following text concisely." Env: `LLM_API_BASE`, `LLM_MODEL` (default `qwen3.8-27b`), `LLM_TIMEOUT_MS` (default 180000, via `AbortSignal.timeout`).
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