Agent Skills

ai-search-gaps-to-social-campaign

★ 84

by seranking

# AI-search gaps → social campaign Use SE Ranking's AI Search data to see which prompts and narratives a brand owns, which competitors own, and which are wide open — then build social content in Planable that stakes a claim in the missing narratives, and instrument it so impact is measurable. > **Scope note (read this).** SE Ranking's AI Search MCP tools expose brand presence, link presence, sha

client-onboarding-proposal

★ 84

by seranking

# Client onboarding proposal Produce the document an agency brings to the start of a client relationship: an honest read of where the client stands across SEO, AI search, and social — and a clear, scoped plan for what the agency will do. It pairs SE Ranking analysis (organic + AI visibility, competitive gaps, site health) with Planable's view of the client's current social performance. > **Posit

seo-sxo

★ 84

by seranking

> Example output: [examples/seo-sxo-bigin-com-20260514/SXO-REPORT.md](../../examples/seo-sxo-bigin-com-20260514/SXO-REPORT.md) # SEO SXO — Search Experience Optimization Diagnose why a "well-optimized" page doesn't rank. Reads the actual SERP for the target keyword, infers the page type Google is rewarding, scores the candidate page against that pattern from multiple persona perspectives, and re

redis-search

✓★ 82

by redis

Redis Search guidance covering FT.CREATE schema design, field type selection (TEXT, TAG, NUMERIC, GEO, GEOSHAPE, VECTOR, JSON path), DIALECT 2 query syntax, FT.SEARCH / FT.AGGREGATE / FT.HYBRID command selection, vector similarity with HNSW or FLAT, hybrid retrieval combining lexical and vector ranking, RAG pipelines, zero-downtime index updates via aliases, and debugging with FT.PROFILE and FT.EXPLAIN. Use when defining a search index on Hash or JSON documents, writing FT.SEARCH queries with fi

iris-development

✓★ 82

by redis

Iris is Redis's umbrella for AI-focused products. Use this skill when integrating with the Iris Redis Agent Memory (RAM) data plane on Redis Cloud — recording session events for an AI agent, creating or searching long-term memories, configuring a memory store, or tuning background memory promotion. Code examples use the official `redis-agent-memory` (Python) and `@redis-iris/agent-memory` (TypeScript) SDKs.

inbox-exploration

★ 49

by posthog

Explore PostHog's Inbox — the surface where signal reports surface as actionable issues and trends. Use when the user asks "what's in my inbox?", "what should I look at?", "which reports are actionable?", "what's PostHog flagged recently?", asks about a specific report by ID or title, or wants to see which signal sources are configured. Covers listing, filtering, and drilling into reports, plus pointers to the deeper `signals` skill when raw signals or semantic search are needed.

querying-posthog-data

★ 49

by posthog

Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query analytics data (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces). Covers HogQL syntax differences from ClickHouse SQL, system table sche

signals

★ 49

by posthog

How to query the document_embeddings table for raw signal data using HogQL. Use when you need to perform semantic search over signals, fetch every signal that contributed to a specific report, or list signal types. For browsing the curated report layer (the Inbox) — listing reports, filtering by status/source, drilling into a single report by ID — use the `inbox-exploration` skill first; drop into this skill afterwards if the user wants the underlying observations.

opensea-api

★ 43

by projectopensea

Query OpenSea marketplace data via the official CLI, MCP server, or shell scripts. Get floor prices, collection stats, NFT details, token data, trending collections, drops, events, and search across Ethereum, Base, Arbitrum, Polygon, and more. Read-only operations; for trading use opensea-marketplace, for token swaps use opensea-swaps.

omni-content-explorer

★ 35

by exploreomni

Find, browse, and organize content in Omni Analytics — dashboards, workbooks, folders, and labels — using the REST API. Use this skill whenever someone wants to find an existing dashboard, search for content, list workbooks, browse folders, see what dashboards exist, find popular reports, download a dashboard as PDF or PNG, favorite content, manage labels on documents, or any variant of "find the dashboard about", "what reports do we have", "show me our dashboards", "where is the sales report",

Nia

★ 24

by nozomio-labs

Index and search code repositories, documentation, research papers, HuggingFace datasets, local folders, Slack workspaces, Google Drive, X (Twitter), and packages with Nia AI. Includes auth bootstrapping, Oracle autonomous research, GitHub live search, Tracer agent, dependency analysis, context sharing, code advisor, document agent, data extraction, filesystem operations, and generic connectors.

search

✓★ 17

by notion

Search the user's Notion workspace using the Notion MCP server. Use for finding pages, databases, and content by keywords or natural-language queries.

research-documentation

✓★ 17

by notion

Search across your Notion workspace, synthesize findings from multiple pages, and create comprehensive research documentation with proper citations and actionable insights.

pinecone-full-text-search

✓★ 14

by pinecone-io

Create, ingest into, and query a Pinecone full-text-search (FTS) index using the preview API (2026-01.alpha, public preview). Use when the user or agent asks to build a text search index on Pinecone, add dense or sparse vector fields, ingest documents, construct score_by clauses (text / query_string / dense_vector / sparse_vector), or compose with text-match filters ($match_phrase / $match_all / $match_any). Ships `scripts/ingest.py` for safe bulk ingestion (batch_upsert + error inspection + rea

pinecone-mcp

✓★ 14

by pinecone-io

Reference for the Pinecone MCP server tools. Documents all available tools - list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents. Use when an agent needs to understand what Pinecone MCP tools are available, how to use them, or what parameters they accept.

browse

✓★ 12

by browserbase

Use the browse CLI for Browserbase browser automation, Browserbase cloud APIs, Browserbase Functions, templates, web fetch/search, diagnostics, and Browse.sh skill discovery/installation. Use when the user asks to navigate pages, inspect browser state, run local or remote browser sessions, manage Browserbase resources, call Browserbase Functions, browse or scaffold Browserbase templates, fetch or search web content, diagnose browse setup, find or install a skill for a website task, discover site

find-skills

★ 12

by agentspace-so

Discover, vet, and install agent skills by searching ACROSS every major registry at once — skills.sh, clawhub.ai, and GitHub — presenting each board on its own native metric (installs / stars) with the top entry per board, security-scanning the top candidates' real SKILL.md for risky patterns, and flagging what's already installed. Use when the user asks "how do I do X", "find a skill for X", "is there a skill that…", "what skill should I install for…", or wants to extend the agent with a capabi

faiss

★ 11

by firecrawl

Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.

sentence-transformers

★ 11

by firecrawl

Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.

pinecone

★ 11

by firecrawl

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

clip

★ 11

by firecrawl

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

qdrant-vector-search

★ 11

by firecrawl

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

chroma

★ 11

by firecrawl

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

firecrawl-search

✓★ 7

by firecrawl

Web search with full page content extraction. Use this skill whenever the user asks to search the web, find articles, research a topic, look something up, find recent news, discover sources, or says "search for", "find me", "look up", "what are people saying about", or "find articles about". Returns real search results with optional full-page markdown — not just snippets. Provides capabilities beyond Claude's built-in WebSearch.

firecrawl-map

✓★ 7

by firecrawl

Discover and list all URLs on a website, with optional search filtering. Use this skill when the user wants to find a specific page on a large site, list all URLs, see the site structure, find where something is on a domain, or says "map the site", "find the URL for", "what pages are on", or "list all pages". Essential when the user knows which site but not which exact page.

firecrawl

✓★ 7

by firecrawl

Search, scrape, and interact with the web via the Firecrawl CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, scrape a webpage, grab content from a URL, get data from a website, crawl documentation, download a site, or interact with pages that need clicks or logins. Also use when they say "fetch this page", "pull the content from", "get the page at https://", or reference external websites. This provides real-time web search

omni-content-explorer

★ 6

by exploreomni

Find, browse, and organize content in Omni Analytics — dashboards, workbooks, folders, and labels — using the REST API. Use this skill whenever someone wants to find an existing dashboard, search for content, list workbooks, browse folders, see what dashboards exist, find popular reports, download a dashboard as PDF or PNG, favorite content, manage labels on documents, or any variant of "find the dashboard about", "what reports do we have", "show me our dashboards", "where is the sales report",

signoz-managing-views

★ 6

by signoz

Use when the user wants to create, list, get, update, rename, or delete a SigNoz saved Explorer view. Trigger on phrases like "save this query as a view", "save this filter", "bookmark this search", "list my saved views", "show me views for traces/logs/metrics/meter", "rename the X view", "update my saved view to also filter Y", "delete the X view", or any request to manage Explorer saved views — even if they don't say "view" explicitly. Also use when someone wants to share a recurring Explorer

signoz-searching-docs

★ 6

by signoz

Look up information in SigNoz documentation. Make sure to use this skill whenever the user asks "how do I", "where in the docs", "what does the docs say about", "find docs for", or otherwise needs reference material on SigNoz instrumentation, OpenTelemetry setup, self-hosted deployment, API endpoints, auth headers, or troubleshooting steps — even if they don't say the word "docs" explicitly. Docs lookup only — for actions inside SigNoz, the agent will pick the matching `signoz-*` action skill.

create-agent-with-sanity-context

★ 4

by sanity-io

Build AI agents with structured access to Sanity content via Sanity Context. Use when setting up a Sanity-powered chatbot, connecting an AI assistant to Sanity content, or adding client-side tools to an agent. Covers Studio setup, agent implementation, and advanced patterns. Always use this skill when users mention building a chatbot with Sanity, creating an AI assistant for their content, setting up the Sanity Context MCP server, integrating Sanity with Claude/GPT/any LLM, making content search