Results for “descript”

Generate full songs and instrumental tracks with ElevenLabs Music on RunComfy via the `runcomfy` CLI. ElevenLabs Music turns a style description plus structured lyrics into studio-quality 44.1 kHz stereo audio — 5 seconds to 5 minutes — with section-level control (Intro / Verse / Chorus / Bridge), multilingual vocals, and commercial-friendly output. Generate a backing track, a full vocal song, a jingle, a podcast intro, a game loop, or an instrumental bed. Calls `runcomfy run elevenlabs/elevenlabs/music-generation` through the local RunComfy CLI. Triggers on "generate music", "make a song", "AI music", "background music", "instrumental track", "ElevenLabs Music", "soundtrack", "jingle", "theme music", "royalty-free music", "compose", or any explicit ask to generate music or a song from a text description.

Use when the user wants to automate SEO tasks using Gemini CLI — meta titles, meta descriptions, alt text, schema markup, keyword research, or bulk page optimization. Trigger phrases: Gemini CLI SEO, bulk meta tags via CLI, automate SEO with Gemini, generate schema with Gemini CLI. For AI-powered SEO strategy see ai-seo.

Discover what bundled skills exist in this mimir install and what each one does. Use when you're not sure if a capability is already covered by a skill ("can I X?", "is there a skill for Y?", "what skills do I have?"). Reads SKILL.md frontmatter from each skill directory and surfaces names + descriptions.

Expert YouTube scriptwriting skill for creators who want to write videos that hold attention, deliver value, and grow a channel. Use this skill whenever the user wants to write or improve a YouTube video script, craft a stronger hook or intro, structure a video idea, write a YouTube title or description, or plan a video outline. Triggers: "write a YouTube script", "help me script my video", "I'm making a video about", "write an intro for my video", "help me with my hook", "write a YouTube description", "how should I structure this video", "make my script more engaging", or any request related to YouTube video content. Do NOT limit to obvious script requests — if the user has a topic or idea and mentions YouTube, use this skill.

Design and validate model-facing tool definitions with clear names, action-oriented descriptions, bounded JSON Schema parameters, explicit side effects, safe defaults, idempotency, errors, and realistic tests. Use when creating function-calling tools, MCP tools, agent actions, structured tool inputs, or when a model selects the wrong tool, invents arguments, or causes unsafe side effects.

Drafts SEC-compliant prospectuses for U.S. securities offerings (IPOs, follow-ons, private placements). Structures cover page, risk factors, use of proceeds, business description, MD&A, financials, and offering terms against uploaded company documents and EDGAR comparables. Use when preparing a Form S-1, S-11, offering circular, or any primary disclosure document for a public or private issuance.

Execute git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping

Drafts a U.S. Bill of Sale for transferring personal property ownership from seller to buyer. Covers vehicles, equipment, watercraft, firearms, business assets, and general personal property. Enforces party identification, property description, consideration, condition representations, title warranty, lien disclosure, and jurisdiction-specific execution requirements. Use when drafting a bill of sale, asset transfer document, or ownership transfer instrument.

Direct TMDL file authoring and BIM-to-TMDL conversion for semantic models in PBIP projects. Automatically invoke when the user asks to "edit TMDL", "add a measure in TMDL", "TMDL syntax", "fix formatString", "fix summarizeBy", "TMDL indentation", "convert BIM to TMDL", "add a column description", "create a calculated column in TMDL", or mentions .tmdl file editing or BIM-to-TMDL migration.

Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaf

Run condition-based vault health diagnostics. 8 categories — schema compliance, orphan detection, link health, description quality, three-space boundaries, processing throughput, stale notes, MOC coherence. 3 modes — quick (schema+orphans+links), full (all 8), three-space (boundary violations only). Returns actionable FAIL/WARN/PASS report with specific fixes ranked by impact. Triggers on "/health", "check vault health", "maintenance report", "what needs fixing".

Drafts a U.S. Lis Pendens (Notice of Pending Action) for recording against real property. Covers intake, statutory authority mapping, legal description, verification, and proof of service. Use when preparing a lis pendens, notice of pending action, or recording notice tied to title or possession claims. Trigger: lis pendens, notice of pending action, quiet title, specific performance, foreclosure, partition, lien enforcement, expungement.

Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/

Checks a GitHub, GitLab, or Perforce (p4) pull request (or merge request, or shelved changelist) for unresolved review comments, failing status checks, and incomplete PR descriptions. Waits for pending checks to complete, categorizes issues as actionable or informational, and optionally fixes and resolves them. Use when the user wants to check a PR/MR/CL, address review feedback, or prepare a change for submission.

Generate Euro NCAP test scenarios and variants using the ADT Euro NCAP support package. Use when creating NCAP seed scenarios, generating variants, translating between drivingScenario and RoadRunner, plotting scenario descriptors, computing NCAP scores, or exporting reports. Triggers on: ncapScenario, euroAssessment, getScenario, getScenarioDescriptor, generateVariants, ScenarioDescriptor, ScenarioDescriptorPlot, ncapScore, ncapReport, exportReport, configureVUT, assessmentTable, Euro NCAP, CCRs, CCRm, CCRb, CCFtap, CCCscp, CPNA, CPFA, CBNA, variant generation.

Generate or edit images with Codex. Use this skill whenever the user says "imagine ...", asks to create an image from a text description, transform or restyle an existing image, produce artwork / illustrations / logos / concept art, make image variations, or asks for any kind of AI image generation or image-to-image editing. All outputs are saved inside the current project's `./images/` folder by default.

Use when a bounded textual artifact (prompt, rubric, tool description, extraction instruction) keeps underperforming and success can be measured with an evaluator, dataset, or trace set. GEPA proposes evaluator-backed candidate rewrites through a normal PR/proposal adoption gate. Do not use for vague behavior changes, governance/persona/core-memory edits, fake metrics, or problems whose first honest task is defining the evaluator or collecting data.

Author pull requests with intent-separated commits, blast-radius disclosure, and verification evidence. Load when opening a PR, writing a PR description, separating refactor from feature commits, or summarizing a change set for review. Also triggers on "write the PR", "PR body", "intent-separated commits", "split refactor from feature", "pr summary", or after safe-change cycles complete. Extends git-workflow-and-versioning — does not replace it.

Capture a visual demo reel (GIF, terminal recording, screenshots) for PR descriptions. Use when shipping UI changes, CLI features, or any work with observable behavior that benefits from visual proof. Also use when asked to add a demo, record a GIF, screenshot a feature, show what changed visually, create a demo reel, capture evidence, add proof to a PR, or create a before/after comparison.

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.

Open a GitHub PR with a tight body — why this change, what'll surprise the reviewer, how we know it's correct — or add a pull_request_template to a repo. Use when asked to open, create, or draft a pull request, compose or regenerate a PR description, or scaffold a repo's PR template. Composes the Summary/Gotchas/Verification structure and detects an existing template first.

Delegates a coding task to pi (dev/pi), a minimal terminal coding harness. This skill should be used when the user asks to "use pi", "run pi", "delegate to pi", "let pi handle this", "ask pi to", "have pi do", or invokes /pi:delegate. It bridges the current Claude Code context to the pi CLI, passing relevant files, git state, and the task description for execution by the pi-agent.

Create an experiment for the MATLAB Experiment Manager app from user code, script, or problem description. TRIGGER when: user asks to create an experiment from a script/function, wants to sweep parameters or hyperparameters, asks to compare configurations, or describes a problem suitable for experimentation. DO NOT TRIGGER when: user is debugging an existing experiment, wants to run/resume trials, or already has a working experiment set up.

Find and query PI assets using MATLAB's Industrial Communication Toolbox. Two paths: (1) direct PI tag lookup via piclient + tags when user provides a tag name or description (R2022a+), (2) Asset Framework navigation via afclient when user needs element/attribute hierarchy (R2026a+). Use when working with PI AF servers, PI Data Archive tags, asset hierarchies, element templates, attribute lookup, or historical data from OSIsoft PI systems.

Design structured evaluation rubrics for scoring LLM and agent outputs — defining quality dimensions, scoring scales, hard gates, score descriptions, and edge cases. Load when the user asks to create an eval rubric, define evaluation criteria, design scoring dimensions, write an eval spec, or says "what should I evaluate", "design a rubric", "create eval criteria", "define quality dimensions", "evaluation rubric for", "how do I measure quality of". Sub-skill of eval-output orchestrator.

Logs the outcome of a skill execution to observations/<skill-name>/runs.md. Trigger on: "log this run", "skill worked", "skill failed", "this didn't work", "log the outcome", "record this", "note that", or after any skill completes with a clear success, partial, or failure outcome. Creates the observations file if it does not exist, then appends an entry with date, task description, skill used, outcome, what worked, what failed, and any error messages observed.

Wraps RDKit in a high-level, pandas-friendly datamol interface with sensible defaults for everyday drug discovery — SMILES/SDF loading into DataFrames, molecule standardization, descriptors, fingerprints, Butina clustering, 3D conformer generation, scaffold analysis, and parallel batch processing, returning native rdkit.Chem.Mol objects. Use when running standard cheminformatics pipelines on molecule tables with minimal boilerplate; for low-level control, custom sanitization, or specialized algorithms prefer alterlab-rdkit. Part of the AlterLab Academic Skills suite.

Teleological physiology analysis framework for understanding biological systems through multi-constraint optimization. Use when analyzing physiological mechanisms, explaining apparent biological "inefficiencies", preparing for medical examinations (CICM/ANZCA Primary), understanding why biological systems are designed the way they are, or when seeking deeper mechanistic understanding beyond descriptive knowledge. Triggers on questions like "why is X designed this way", "what purpose does Y serve", "how is Z optimized", analysis of physiological trade-offs, or exploration of evolutionary/design constraints.

Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one. Source: github.com/emilkowalski/skills.

Generates commit messages following the gitmoji convention (https://gitmoji.dev) — picks the right emoji for the intent of the change and writes a well-formed message. Use when asked to "write a gitmoji commit", "add an emoji to my commit message", "which gitmoji should I use", "gitmoji this change", or when a project uses gitmoji-style commit messages. Works from a git diff, staged changes, or a plain description of the change. Generates the message only — does not run git commands.

When tackling a task that requires domain expertise beyond general coding ability — architecture patterns, framework-specific gotchas, deployment strategies, security anti-patterns, or specialized workflows — call the windags_skill_graft MCP tool with your task description. You'll receive expert knowledge including decision trees, failure modes, worked examples, and anti-patterns from a library of 503+ curated skills. Only graft when the task genuinely requires specialized knowledge. Simple tasks (rename a variable, fix a typo, format code) do not need grafting.

Breaks natural-language problem descriptions into sub-tasks suitable for DAG nodes. The entry point of the meta-DAG. Identifies phases, dependencies, parallelization opportunities, and vague/pluripotent nodes that can't yet be specified. Uses domain meta-skills when available. Activate on "decompose task", "break down problem", "plan workflow", "what are the steps", "sub-tasks", "task breakdown". NOT for executing the decomposed tasks (use dag-runtime), building the DAG structure (use dag-planner), or matching skills to nodes (use dag-skills-matcher).

Triage a raw, vague issue or bug report into a structured document that names what is known, what is missing, and what to do next. Use when an incoming issue, bug report, or problem description is too vague or incomplete for investigation or planning, and recommend the right next han skill. Does not investigate root causes or trace code paths — use investigate for debugging, diagnosis, and root cause analysis. Does not plan features or build solutions — use plan-a-feature or plan-implementation for that.

Guide for writing the abstract of an academic economics paper. Use this skill whenever the user asks for help writing, drafting, revising, or structuring an abstract for an economics paper - whether empirical micro, development economics, applied economics, or related fields. Also trigger when the user mentions "abstract," "paper summary," or asks how to compress their findings into a short description. This skill synthesizes best practices from David Evans (CGDev), Marc Bellemare, and patterns observed in top economics journals (AER, QJE, AEJ: Applied, etc.).

Builds a new Claude Code skill from scratch through a relentless, evidence-based interview that walks the skill's design tree decision-by-decision — entity fit, use cases, name, description, workflow steps, tools, and progressive-disclosure layout — then reviews the finished skill against the plugin-building guidance and applies every fix it finds. Use when creating, authoring, scaffolding, designing, or drafting a new skill or slash command. Does not build an agent or subagent — use agent-builder. Does not serve, vendor, or refresh the authoring guidance itself — use guidance.