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Found 75 Skills
Expert B2B list building orchestrator for outbound sales campaigns. Use when the user asks about building lead lists, Sales Navigator search, boolean filters, ICP definition, ICP scoring, lead sources, data validation, email verification, list segmentation, Apollo prospecting, Clay Find People, list hygiene, deduplication, account qualification, ABM lists, or assembling prospect lists for cold outreach. Also triggers on "lead list", "list building", "Sales Navigator", "boolean search", "ICP", "ideal customer profile", "find leads", "prospect list", "lead source", "email verification", "data validation", "list hygiene", "Evaboot", "PhantomBuster", "export leads", "build a list", "find prospects", "deduplicate", "qualify accounts", "ABM". Do NOT use for enrichment workflows (use clay skill) or email writing (use cold-email skill).
DEFAULT search tool for ALL search/lookup needs. Multi-source search and deduplication layer with intent-aware scoring. Integrates Brave Search (web_search), Exa, Tavily, and Grok to provide high-coverage, high-quality results. Automatically classifies query intent and adjusts search strategy, scoring weights, and result synthesis. Use for ANY query that requires web search — factual lookups, research, news, comparisons, resource finding, "what is X", status checks, etc. Do NOT use raw web_search directly; always route through this skill.
Guides cleaning and standardizing tabular datasets before analysis, modeling, or reporting—profiling, quality rules, missing values, duplicates, outliers, type coercion, encoding fixes, record linkage, deduplication, high-level PII handling (not legal advice), actuarial/insurance field scrubbing, reproducible scrub pipelines, validation checks, and sign-off. Distinct from warehouse ETL or statistical modeling. Use when the user asks for "data scrubbing", "clean this dataset", "scrub the data", "data cleaning", "dedupe records", "handle missing values", "outlier treatment", "standardize columns", "data quality rules", "profile this table", or "prepare data for modeling". Not warehouse pipelines (data-warehouse-engineer), ML modeling (data-scientist, actuary), privacy programs (compliance-engineer), FinOps only (finops-analyst), or assumption governance (assumption-setting).
Build messaging agents and apps with Spectrum — Photon's unified messaging SDK. Write your handler logic once and ship it across iMessage, WhatsApp Business, the terminal, or a custom platform. Spectrum is multi-platform by design and is becoming multi-language; the current SDK is `spectrum-ts` (TypeScript), with additional language SDKs planned. Use this skill for any Spectrum question — quickstart, multi-platform setup, receiving messages, content builders, spaces and users, reactions and replies, platform narrowing, the built-in providers (iMessage cloud/local/dedicated with message effects, Terminal TUI test harness, WhatsApp Business 1:1), custom event streams, graceful shutdown, building your own provider with `definePlatform`, and the production architecture patterns Photon uses internally to ship agents that live natively inside IM apps (five-stage inbound pipeline with debounce → batch flush → mark as read → generate → send, in-flight cancellation with abort signals, drain-in-handler, carry-forward, idempotent retries via stable client GUIDs and a startIndex resume cursor, per-resource memory scope `resourceId` vs `threadId`, durable job-failure audit log). This is the entry point for the skill; consult the topic files in this directory for full reference. Keywords: spectrum, spectrum-ts, photon, unified messaging, multi-platform, multi-language, im agent, messaging agent, imessage, whatsapp, whatsapp business, terminal, tuichat, definePlatform, custom platform, platform provider, platform narrowing, app.messages, Spectrum(), space, send, reply, react, tapback, typing indicator, responding, startTyping, stopTyping, content builder, text, attachment, voice, contact, richlink, poll, group, custom content, message effects, bubble effect, screen effect, line model, dedicated line, shared pool, custom events, app.stop, lifecycle, SIGINT, graceful shutdown, message queue, debounce, batch, in-flight, cancellation, abort controller, carry forward, idempotent retry, client guid, dedup, deduplication, startIndex, resume cursor, working memory, resourceId, threadId, per-resource memory, job failure, audit log, race condition, worker crash, retry, pg-boss, queue worker, conversational agent, chat agent, native messaging, agent architecture, production agent, spectrum patterns, best practices.
Save structured content to Obsidian vault with standardized frontmatter, folder routing, deduplication, and wikilink generation. Persists res-deep research, res-price-compare reports, and generic content. Use when: saving to vault, persisting results, store in obsidian. Triggers: save to vault, vault save, persist this, save research, store in vault, update vault note.
Cosmos-Embed1 video-text embedding for text-to-video retrieval, video-to-video search, semantic deduplication, and fine-tuning. Use when the user asks to "fine-tune Cosmos-Embed1", "run cosmos-embed inference", "export Cosmos-Embed1", "embed videos", or "search videos with text".
Find leads matching criteria and bulk-add them to an Apollo outreach sequence. Handles enrichment, contact creation, deduplication, and enrollment in one flow.
Configure Prometheus Alertmanager with routing trees, receivers (Slack, PagerDuty, email), inhibition rules, silences, and notification templates for actionable incident alerting. Use when implementing proactive monitoring with automated incident detection, routing alerts to the appropriate team by severity, reducing alert fatigue through grouping and deduplication, integrating with on-call systems like PagerDuty, or migrating from legacy alerting to Prometheus-based alerting.
Use this skill whenever building, reviewing, or refactoring React components that fetch data from APIs — especially at scale (recommender carousels, infinite feeds, pages with many parallel fetches, dashboards). Covers request orchestration (parallelism, batching, deduplication), cache strategy (keys, normalization, staleTime, SWR), backend protection (concurrency caps, debounce/throttle, jittered retries, circuit breakers), prefetching (route loaders, hover/intent, idle, server hydration), failure resilience (AbortController, timeouts, error boundaries, stale fallback, idempotent mutations), and feed/carousel patterns (virtualization, cursor pagination, summary/detail split). Trigger even if the user doesn't explicitly mention "performance" or "scale" — any non-trivial React data-fetching code benefits from these patterns. Includes 5 ready-to-use scaffolding templates (resource query hook, carousel data loader, infinite feed, hover-prefetch link, request collapser).
Design and operate an advanced AI agent memory system on HelixDB using hybrid graph + vector + BM25 search. Use when building long-term memory, user profiles, document/chunk RAG, recall/remember features, memory extraction, deduplication, consolidation, versioning, updating, forgetting/deletion, categorisation, or connector-backed ingestion. Covers tenant-safe Helix data modeling, modality decision rules, the full write/maintain lifecycle, and the product layers an agent must implement around Helix. TypeScript-first (@helix-db/helix-db); a Rust DSL variant is in EXAMPLES.rust.md.
Set up @personize/signal — a smart notification engine that decides IF, WHAT, WHEN, and HOW to notify each person using Personize memory and governance. Guides you through connecting event sources, configuring delivery channels, setting up governance rules, and testing the decision engine. Use this skill whenever the user wants to build smart notifications, AI-powered alerts, notification fatigue prevention, daily/weekly digests, personalized messaging, or intelligent notification routing. Also trigger when they mention @personize/signal, notification scoring, quiet hours, deduplication, channel routing (email vs Slack vs in-app vs SMS), or want notifications that know when to stay silent.
Knowledge graph memory orchestration - entity extraction, query parsing, deduplication, and cross-reference boosting. Use when designing memory orchestration.