recoup-trend-jack
Turn a real, timely trigger into content for an artist — a fresh milestone (crossed 2B streams, added to a flagship playlist, a TV/film sync, a chart entry) or a current trend/sound. Use when the user says "they just hit [X], make something", "make something for the milestone", "what's the move right now", "make it feel current/timely", or "react to [news]". Reads the artist's research feed to find the real moment, picks the angle, then routes the actual asset to the right content skill (promo graphic / caption / short video). Not for evergreen "make a post" requests with no trigger — use the router for those.
What this skill does
# Recoup Trend-Jack (reactive content)
Most content skills answer "make me a [format]." This one answers a different question:
**"something is happening — what should we make of it?"** The trigger is *news*, not a
format. The job is to find the real moment, choose the angle, and hand the actual asset to
the skill that makes it. Never manufacture a moment that isn't real.
Scripts and references ship alongside this skill in its own `references/`; read them with
relative paths.
## When this fires vs. the router
- "Make a TikTok for Mari Vega" → a **format** request → router → `recoup-short-video`.
- "Mari just crossed 1M monthly listeners, make something" → a **trigger** → **this skill**.
- "What's the move for her right now?" → a **trigger** (find one) → **this skill**.
If the user already named both the trigger *and* the format ("milestone graphic for the 2B
streams"), you can skip straight to the format skill with the angle in hand — but still
verify the milestone is real first (step 2).
## Workflow
### 1. Resolve the artist + read context
Standard backbone. Resolve the workspace and IDs (see the account-resolver reference), read
`context/artist.md` (voice) and `context/audience.md` (how fans talk). These set the tone of
whatever you end up making.
### 2. Find the real trigger (don't invent one)
Pull the research feed and look for something genuinely fresh. See the research-context
reference for the auth + provider-ID chain and the `research_get` retry helper.
```bash
# Latest milestone (often empty; inspect fields — naming varies):
research_get "milestones?id=$PROVIDER_ID" \
| jq -r 'if (.milestones|length)>0 then .milestones[-1] else "no fresh milestone" end'
# Recent activity feed — chart entries, syncs, co-signs (note: under .career, not .activities):
research_get "career?id=$PROVIDER_ID" \
| jq -r '.career[0:5][]? | "\(.activity_date): \(.activity_text)"'
# A real playlist placement worth bragging about (followers_total is a string like "9.09M"):
research_get "playlists?id=$PROVIDER_ID" | jq -r '.placements[0:3][]? | .playlist_name'
```
For a *trend* (no milestone, "make it current") pull lightweight web intelligence and use it
only as **direction** for the format/look — never as a source of facts:
```bash
research_get "web?q=$(printf %s "$ARTIST_NAME current trends" | jq -sRr @uri)" | jq -r '.summary // .content'
```
**Triage the result:**
- **Fresh, real milestone/event** → that's the headline. Continue.
- **Only stale milestones (months old)** → tell the user there's no fresh moment; offer an
evergreen post or a different angle instead of faking urgency.
- **Trend only** → use it to pick a timely format/reference; keep the facts from context.
### 3. Pick the angle + the format
Match the trigger to the asset that carries it best:
| Trigger | Best format → skill |
| --- | --- |
| Streams / followers milestone, playlist add, chart entry | `recoup-promo-graphic` (a "the numbers" graphic) |
| Sync / press / co-sign, or a story to tell | `recoup-brand-voice-caption` (+ a graphic) |
| A sound/format trend, "make it move" | `recoup-short-video` (let the song's audio drive the edit) |
Write a one-line angle in the artist's voice ("2,000,000,000 streams. thank you 🤍") drawn
from `context/artist.md`, then hand off.
### 4. Route to the format skill — don't re-implement it
Invoke the chosen `recoup-content-*` skill with the trigger + angle as inputs. It already
owns generation, the audio→edit mapping, and the analyze gate. Your job ends at a correct,
real, well-framed brief plus the routing.
### 5. Verify + write back
Whatever asset comes back goes through the analyze gate (benchmarked against the artist's top
recent posts when available — see the analyze-gate reference) and is written into the
workspace with a `{what}: {why}` commit. A reactive post that ships late or off-fact is worse
than none — accuracy and timing are the whole point.
## Guardrails
- **Real or nothing.** Every number, date, and claim traces to the research feed or the
workspace. If you can't verify the trigger, say so; don't generate around a guess.
- **Don't double-spend.** This skill *routes*; it doesn't run a second generation pipeline.
- **Scope stops at the asset.** Like the rest of the plugin, this produces the finished
asset + caption — it does not post or schedule.
Related in Image & Video
watch
IncludedWatch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or Whisper API fallback), and hands the result to Claude so it can answer questions about what's in the video.
physical-ai-defect-image-generation
IncludedUse when the user wants to orchestrate defect image generation, run associated setup, or handle outputs on OSMO. The Day 0 path handles cold-start with USD-to-ROI, image-edit augmentation, and AnomalyGen to create initial PCBA datasets. The Day 1 path performs inference and labeling on real images. This skill helps with first-time asset setup, creation of finetuning checkpoints, and configuring deployment. Trigger keywords: defect image generation, dig workflow, dig pipeline, defect image detection workflow, aoi pipeline, aoi anomalygen, usd2roi anomalygen, day 0 pcba, day 1 pcba, day 1 real-photo alignment, day 1 manual roi, metal surface anomaly, glass defect, anomalygen finetune, setup_pcb, setup_metal, setup_glass, setup_pretrained, dig setup, dig datasets, dig pretrained checkpoint, dig image-edit endpoint.
accelint-react-best-practices
IncludedReact performance optimization and best practices. ALWAYS use this skill when working with any React code - writing components, hooks, JSX; refactoring; optimizing re-renders, memoization, state management; reviewing for performance; fixing hydration mismatches; debugging infinite re-renders, stale closures, input focus loss, animations restarting; preventing remounting; implementing transitions, lazy initialization, effect dependencies. Even simple React tasks benefit from these patterns. Covers React 19+ (useEffectEvent, Activity, ref props). Triggers - useEffect, useState, useMemo, useCallback, memo, inline components, nested components, components inside components, re-render, performance, hydration, SSR, Next.js, useDeferredValue, combined hooks.
elevenlabs-agents
IncludedBuild conversational AI voice agents with ElevenLabs Platform using React, JavaScript, React Native, or Swift SDKs. Configure agents, tools (client/server/MCP), RAG knowledge bases, multi-voice, and Scribe real-time STT. Use when: building voice chat interfaces, implementing AI phone agents with Twilio, configuring agent workflows or tools, adding RAG knowledge bases, testing with CLI "agents as code", or troubleshooting deprecated @11labs packages, Android audio cutoff, CSP violations, dynamic variables, or WebRTC config. Keywords: ElevenLabs Agents, ElevenLabs voice agents, AI voice agents, conversational AI, @elevenlabs/react, @elevenlabs/client, @elevenlabs/react-native, @elevenlabs/elevenlabs-js, @elevenlabs/agents-cli, elevenlabs SDK, voice AI, TTS, text-to-speech, ASR, speech recognition, turn-taking model, WebRTC voice, WebSocket voice, ElevenLabs conversation, agent system prompt, agent tools, agent knowledge base, RAG voice agents, multi-voice agents, pronunciation dictionary, voice speed control, elevenlabs scribe, @11labs deprecated, Android audio cutoff, CSP violation elevenlabs, dynamic variables elevenlabs, case-sensitive tool names, webhook authentication
humanizer
IncludedHumanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 28 pattern detectors, 560+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.
generating-mermaid-diagrams
IncludedSalesforce architecture diagrams using Mermaid with ASCII fallback. Use this skill when generating text-based diagrams for Salesforce architecture, OAuth flows, ERDs, integration sequences, or Agentforce structure. TRIGGER when: user says "diagram", "visualize", "ERD", or asks for sequence diagrams, flowcharts, class diagrams, or architecture visualizations in Mermaid. DO NOT TRIGGER when: user wants PNG/SVG image output (use generating-visual-diagrams), or asks about non-Salesforce systems.