Loading...
Loading...
Found 540 Skills
E-commerce email marketing system builder. Creates complete email automation flows with full copywriting, subject lines, ESP setup instructions, segmentation rules, and annual campaign calendars. Generates copy-paste-ready email sequences for Klaviyo, Omnisend, Mailchimp, or any ESP. Covers welcome series, cart abandonment, browse abandonment, post-purchase, review requests, cross-sell, win-back, VIP/loyalty, replenishment, and sunset flows. Includes A/B test subject line variants, send timing, trigger conditions, branching logic, and seasonal campaign calendar. No API key required. Use when: (1) setting up email marketing for an e-commerce store, (2) writing email sequences and flows, (3) planning seasonal email campaigns.
Qualifies inbound leads against full ICP criteria — company size, industry, use case fit, role/seniority of the person. Checks CRM and existing customer base for duplicates and existing relationships. Outputs a scored CSV with qualification status, reasoning, and pipeline overlap flags. Tool-agnostic — works with any CRM, enrichment tool, or data source.
Write high-converting cold emails using structured frameworks, personalization tiers, and patterns from real campaigns. Pure reasoning skill — no scripts. Auto-loads when any task requires outreach copy.
LoRA, full fine-tuning, DPO preference tuning, VLM training, function-calling tuning, reasoning tuning, and BYOM uploads on Together AI. Reach for it whenever the user wants to adapt a model on custom data rather than only run inference, evaluate outputs, or host an existing model.
This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration. Part of the context engineering skill suite — also activates when the user mentions "context engineering" or "context-engineering" in the context of belief-based agent reasoning.
A method for iteratively improving text instructions for agents (skills / slash commands / task prompts / CLAUDE.md sections / code generation prompts) by having unbiased executors run them, then evaluating from both perspectives (executor self-report + instruction-side metrics). Repeat until improvement plateaus. Use immediately after creating or significantly revising a prompt or skill, or when you suspect the reason an agent isn't behaving as expected is due to ambiguity in the instructions.
Simulate target-conference reviewers for an ML/AI paper before submission. Use this skill whenever the user wants a reviewer-style critique, predicted scores, likely reject reasons, rebuttal risks, area-chair style meta-review, adversarial Reviewer 2 feedback, or venue-specific pre-review for conferences such as NeurIPS, ICML, ICLR, CVPR, ACL, EMNLP, or similar venues. This skill should dynamically inspect reviewer guidelines, example reviews, accepted papers, and project evidence when available.
Every Open-Meteo endpoint family in one CLI — forecast, archive, marine, air quality, flood, climate, ensemble, seasonal, geocoding, elevation. Trigger phrases: `what's the weather in`, `forecast for`, `is it going to rain`, `marine forecast`, `air quality in`, `historical weather`, `climate normal`, `use open-meteo`, `run open-meteo`.
Plan an Israeli wedding from engagement to chuppah, covering venue selection (ulmot, ganot aruim), vendor comparison via Israeli platforms (Celebrate, Engaged, Save A Date, Walla Wedding), budget planning (~100-140K NIS average), Rabbinate registration (tik nisuin, teudat ravakut), halachic requirements (mikveh, ketuba), guest management, per-plate cost optimization, seasonal pricing, and timeline creation. Use when user asks about "chatuna b'yisrael", Israeli wedding planning, wedding budget, "ulam aruim", "ulmot", "ganim", wedding vendors, Rabbinate requirements, "tik nisuin", ketuba, or wedding timeline. Prevents common mistakes like missing Rabbinate deadlines, overpaying on Thursday weddings, or forgetting AKUM fees. Do NOT use for destination weddings abroad, non-Jewish religious ceremonies, or divorce proceedings.
High-converting landing pages — campaign pages, collection pages, seasonal promos, A/B testing
Compares competing PRs that target the same issue and recommends which one to merge. Runs gate, correctness, and quality checks; outputs a deterministic scorecard with reasoning trace. Use when an issue has two or more open PRs and a maintainer needs to decide which to merge.
INTERNAL sub-agent for blind 9-dimensional rubric scoring. **NOT a user-facing skill — do NOT invoke from the main conversation.** It is called via the Task tool by cheat-score / cheat-predict / cheat-bump to generate a context-isolated score for a script. It ONLY accepts script_path + rubric_notes_path; any other input will be refused. It outputs strict JSON: 9 dimensions × {score 0-5, confidence enum, one-line reason}. **It strictly refuses to read** .cheat-state.json, predictions/*, retro sections, or any content that may leak post-publish data. This is Channel B in the 3-channel calibration model (A=main, B=blind sub-agent, C=cross-model).