Results for “ai-first”

When the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions "attribution," "attribution model," "first-touch vs last-touch," "multi-touch," "which channel drives revenue," "what's my real CAC," "my dashboards disagree," "Google/Meta says X but GA says Y," "media mix model," "MMM," "incrementality," "geo lift," "holdout test," "how did you hear about us," "self-reported attribution," "dark social," or wants to instrument attribution themselves — "stitch my bookings to their source," "SavvyCal/Calendly attribution," "close the identify gap," "track conversions on a third-party domain," "first-party / self-hosted attribution." For event tracking setup and UTMs, see analytics. For ad-platform pixels/CAPI, see ads. For pipeline and CRM revenue reporting, see revops. For the AI-search attribution blind spot, see ai-seo.

Use this skill when a user asks for SEO keyword research, keyword discovery, search volume analysis, keyword difficulty, search intent mapping, topic clusters, content opportunities, competitor keyword gaps, or a keyword strategy for a domain, URL, product, market, or seed topic. When a website is provided, crawl and interpret the site first, then use AIsa API access to DataForSEO keyword, SERP, trend, Labs, and OnPage endpoints plus AIsa LLM reasoning to find non-brand keyword opportunities. Use when: the user needs web search, research, source discovery, or content extraction.

Work with Lapian Notes / 拉片笔记 (github.com/bkingfilm/lapian-notes) — a local- first React/Vite tool that turns a film into an editable shot-by-shot study notebook: local frame extraction, AI-assisted structure analysis (bring your own AI, no API key required), story-line swimlane timeline, structure tree, and audience-emotion curve. Use when the user asks about Lapian Notes, "拉片笔记", "拉片" (shot-by-shot film analysis) tooling, cloning/running this repo (npm run dev, run.bat/run.command), the AI-analysis-package (ZIP) round-trip workflow, or contributing a PR to lapian-notes. Not for generic video editing (use `opencut` for that) or generic film-analysis theory unrelated to this codebase.

Onboard AI agents into the Senpi trading platform on Hyperliquid. Walks through account creation, API key setup, MCP server configuration, skill discovery, and first-trade guidance. Includes a mandatory skill update check that surfaces new or updated skills after onboarding. Use when user says "set up Senpi", "get started with Senpi", "connect to Senpi", "install Senpi", "onboard to Senpi", or when an agent needs to start trading on Hyperliquid via Senpi.

Strip AI-generated tells from a draft and rewrite it in Heath's personal voice (GTM Juice). Fire whenever the user asks to "humanize", "de-AI", "clean up", "rewrite in my voice", "make this sound like me", "kill the AI tells", or "polish this draft". Also fires as the mandatory final pass inside juice-content-dispatch — every LinkedIn post, newsletter, and video script drafted there must run through this skill before the Notion write. Applies the 29-pattern Wikipedia "Signs of AI writing" catalog (em dashes, significance inflation, promotional language, rule of three, synonym cycling, inline-header lists, sycophantic tone, hyphenated word pairs, persuasive-authority tropes, signposting, etc.), then applies a hardcoded Heath-voice calibration: self-implicating first, failure-derived, plain declaratives over bumper-sticker aphorisms, no bow-tied endings, never Mixmax corporate brand voice. Forked from blader/humanizer (MIT).

Routing-first visual approval gate for AI agent plans, markdown specs, and diffs. Use when a human needs to review a concrete plan before execution, inspect a targeted diff in a browser, mark up a spec/PRD/architecture note, or set up the review loop on Claude Code, Gemini CLI, Codex CLI, or OpenCode. Route planning/spec creation to `task-planning` or `ralph`, broad PR-policy review to `code-review`, rendered-UI critique to `agentation`, and fresh-session browser verification to `browser-harness`.

Chief Security Officer mode. Infrastructure-first security audit: secrets archaeology, dependency supply chain, CI/CD pipeline security, LLM/AI security, skill supply chain scanning, plus OWASP Top 10, STRIDE threat modeling, and active verification. Two modes: daily (zero-noise, 8/10 confidence gate) and comprehensive (monthly deep scan, 2/10 bar). Trend tracking across audit runs. Use when: "security audit", "threat model", "pentest review", "OWASP", "CSO review". (gstack) Voice triggers (speech-to-text aliases): "see-so", "see so", "security review", "security check", "vulnerability scan", "run security".

Install and route through OpenSpace, the skill management layer for AI agents, so a host agent can retrieve/rank/load the right SKILL.md out of this jeo-skills catalog (~150 installed skills), then evaluate skill quality from real execution evidence and evolve skills via FIX/DERIVED/CAPTURED updates. Covers install-as-skill-finder, retrieve-a-skill, evaluate-quality, evolve-skills, and local-first hub share/import. Triggers on: openspace, skill finder, skill retrieval, find the right skill, rank skills, skill discovery, skill quality, evolve skill, FIX DERIVED CAPTURED, skill hub, openspace-mcp, DiscoverSkills, skill-discovery, delegate-task.

Anti-slop frontend design skill for building NEW landing pages, portfolios, and UI from a brief. Reads the brief and existing brand context first, infers a design direction from three configurable dials (variance, motion, density), and ships interfaces that don't look templated or default-AI. Use when starting a new page/UI with no existing identity to preserve, or when asked to design something "that doesn't look like every other AI landing page." For auditing and upgrading an EXISTING UI, use `taste-redesign` instead — this skill is for the moment before code exists. Triggers on: "build a landing page", "design a portfolio", "make this not look generic/templated/AI-generated", "anti-slop", starting UI work with a brief but no established identity yet. Also owns component-library selection (which package for toasts, dropdowns, charts, drag-and-drop, state, etc.) — use this instead of hand-rolling a component or guessing at a dependency. Source: github.com/Leonxlnx/taste-skill (taste-skill, the main v2 varian

Use when building a scroll-driven cinematic 3D landing experience for any brand: AI-generated photoreal frames displaced into real 3D by depth maps, a scroll-scrubbed camera fly-through with weighted-leg pacing and per-leg camera moves, mouse parallax, explode-on-scroll shatter transitions, an ember particle field, warp streaks, a live in-scene terminal that runs a REAL product demo, a branded preloader, an exit handoff into the page below, a self-hosted display typeface, phone gyro parallax, and opt-in synthesized ambient sound. Covers 2K image generation via OpenRouter, depth maps, the WebGL engine, chrome (nav/consent) integration, the observe-first verify loop, and the flag-off ship posture.

Expert SOC 2 compliance assistant covering all five Trust Services Criteria (Security/CC, Availability/A, Confidentiality/C, Processing Integrity/PI, Privacy/P). Use this skill whenever a user mentions SOC 2, Trust Services Criteria, SOC 2 Type 1 or Type 2, audit readiness, compliance gaps, control documentation, evidence collection, vendor risk questionnaires, or anything related to AICPA service organization controls. Trigger even for adjacent topics like "we need to get audited", "a customer asked for our security report", "writing an information security policy", or "preparing for an audit". Covers gap analysis, policy writing, control documentation, audit evidence preparation, and vendor risk reviews for organizations at any maturity level — from first-time startups to seasoned compliance teams.

Drive Moli (`moli`), Lexmount's open-source headless browser for AI agents, built around on-demand rendering: real JavaScript, DOM, and CSS by default, with layout and pixels computed only when explicitly requested via `--layout`. Use when the user wants to fetch/extract a live JavaScript-rendered page as Markdown/HTML/JSON/semantic-tree, capture a screenshot or PDF, run a small bounded crawl, start a CDP/WebDriver automation server for Playwright/Puppeteer, replace a Chromium/ChromeDriver dependency, or diagnose readiness/network/frame issues on a rendered page. Triggers on: "moli fetch", "moli serve", "headless browser for agents", "on-demand rendering browser", "CDP server without Chrome", "structure-first web scraping", "Lexmount browser", "moli-webfetch", "moli-cdp-server".

Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.

Set up pre-commit hooks on a Python project — standard file-hygiene checks plus a security gate (gitleaks secret scanning, Trivy filesystem scan for CVEs/secrets/misconfigs, and Bandit Python SAST). Use this whenever the user wants to add, configure, or fix pre-commit hooks on a Python repo, mentions .pre-commit-config.yaml, wants secret/vulnerability/SAST scanning on commits, or is setting up code-quality guardrails — even if they just say 'add pre-commit hooks' without naming the tools. Especially for uv-based GenAI/LLM backends. Handles the setup gotchas that break first-time installs: the Trivy binary, the required data/html.tpl report template, bandit[toml] + [tool.bandit] config, and the right .gitignore entries.

Configure, deploy, and manage Senpi Trading Runtime (OpenClaw plugin @senpi-ai/runtime) for automated on-chain position tracking with DSL trailing stop-loss protection. Use when a user needs to create or modify runtime YAML files, configure DSL (Dynamic Stop-Loss) exit engine parameters (phases, tiers, time-based cuts), set up the position_tracker scanner to monitor a wallet's positions on Hyperliquid, install/list/delete runtimes via CLI, or inspect DSL-tracked positions. The runtime does NOT create strategy wallets; create/get the strategy wallet via Senpi MCP first, then link that existing wallet in runtime YAML. Triggers on mentions of senpi, Senpi runtime, DSL exit, stop-loss tiers, position tracker, trailing stop, openclaw senpi, dsl_preset, or strategy YAML configuration."

Run Comet's Opik — open-source LLM observability, evaluation, and optimization — from one routing-first skill: install the Python/TypeScript SDK, stand up a server (Comet.com cloud, Docker Compose via `./opik.sh`, or Kubernetes/Helm), wire tracing through `@opik.track` or one of 50+ framework integrations (OpenAI, Anthropic, LangChain, LangGraph, LlamaIndex, CrewAI, DSPy, Ollama, Bedrock, Vercel AI SDK, …), score outputs with LLM-as-a-judge metrics (Hallucination, Moderation, Answer Relevance, Context Precision), and run Datasets/Experiments evaluations including PyTest CI gates. Use when the user wants LLM tracing, prompt evaluation, production LLM monitoring, agent optimization, or guardrails with Opik. Triggers on: opik, comet opik, opik configure, opik.sh, llm observability, llm tracing, llm as a judge, hallucination metric, prompt evaluation, opik dashboard, opik guardrails, agent optimizer.

Generate images with Flux 2 Klein (Black Forest Labs' distilled fast variant of Flux 2) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents Flux 2 Klein's strengths (sub-second latency, multi-reference brand styling, declarative subject-first prompts), the step-count strategy (4–8 for fast iteration, ~25 for polish), the 9B vs 4B variant trade-off, and when to route to Flux 2 Pro / Seedream 5 / GPT Image 2 instead. Calls `runcomfy run blackforestlabs/flux-2-klein/9b/text-to-image` (or `/4b/`) through the local RunComfy CLI. Triggers on "flux 2 klein", "flux-2-klein", "flux klein", "BFL flux 2", or any explicit ask to generate with this model.

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST

Turn any rough prompt, half-formed idea, or task description into a finished, ready-to-send prompt optimized for Opus 4.8 (with adaptive thinking) inside the chat app — claude.ai, the Mac app, the iOS app — NOT the API. Use this skill whenever the user wants to write, rewrite, optimize, improve, sharpen, or polish a prompt for the chat app. Trigger phrases include "rewrite this prompt", "make this a better prompt", "optimize this prompt", "turn this into a prompt", "help me prompt this", "draft a prompt that...", "I want to ask...", or whenever the user pastes a draft prompt and asks for improvements. Also trigger when the user describes a task they plan to send into the chat app and clearly wants a reusable, well-structured prompt rather than a direct answer. The output is always a single, copy-pasteable prompt in a code block that the user sends as-is — never a template with placeholders. When the request concerns the user's own work, the skill retrieves the real specifics first — memory, meeting transcript