Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
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When the user wants to pay off debt, compare the avalanche vs. snowball strategy, model a payoff timeline, calculate total interest paid, or find the fastest way out of debt. Also use when the user mentions "credit card debt," "student loans," "debt free," "minimum payments," "how long to pay off," or "which debt first."
Cross-protocol DeFi position aggregator for Stacks wallets — 5 parallel scanners covering Bitflow HODLMM LP bins, Zest lending/borrowing (V2 pool-borrow-v2-3), ALEX pool shares, Styx bridge deposits, and Hiro wallet balances. Produces a unified portfolio view with USD estimation (CoinGecko) and risk scoring.
Pre-drafting intake for demand letters — parties, facts, grounds, leverage, best alternative to a negotiated agreement, and confidentiality filtering — written to a structured engagement record file that the demand-letter drafting skill reads. Use when the user wants to prepare a demand letter, do a pre-drafting engagement intake, or get background for demand letters such as payment reminders, breach/remediation demands, or cease-and-desist notices.
Triage of incoming letters — extract key fields, cross-search the matter portfolio, assess substantive merits, propose response options with recommendations, and hand off to matter intake or demand-letter drafting when needed. Use when the user says "received a demand letter", "review this incoming letter", or attaches a letter for assessment.
Investigate commercial, operational, financial, strategic, and technology claims for acquisition, investment, partnership, or vendor decisions. Use for data-room analysis, quality of earnings, customer concentration, working capital, synergies, and diligence recommendations
Activate when an investor feels anxiety, fear or greed from stock price swings and needs to return to rational decision-making. Typical triggers: panic-selling during a crash, wondering whether to take profit during a rally, feeling unbalanced when comparing returns with others, lying awake because the money invested was not spare cash. Language signals: "should I take profit", "I'm so panicked after such a drop", "others made so much and I didn't", "my heartbeat follows the stock price". Not applicable to: pure information lookup (asking for a company's financial data), explicit buy/sell decision logic (that is the circle-of-competence or opportunity-cost skill), short-term trading strategy advice. Distinction from adjacent skills: the circle-of-competence skill addresses the cognitive question of "do I understand it"; this skill addresses the emotion–cognition disconnect of "I understand it but my emotions are still out of control"; the Stop Doing List is the pre-event line of discipline, this skill is the in-event emotional recovery mechanism.
Make an evidence-based hiring decision and produce a Candidate Evaluation Decision Pack (criteria + scorecard, signal log, work sample/trial plan + rubric, reference check script + summary, decision memo). Use for candidate evaluation, hiring decisions, reference checks, work samples/take-homes, and hiring bar calibration. Category: Hiring & Teams.
Simulates a senior-year (Grade 12) liberal arts tutor who coaches politics, history, geography and other integrated liberal arts subjects with a heuristic teaching approach. Focuses on developing understanding, memory and analytical skills. Use when a student asks a liberal arts question or requests explanations of historical events, geographical phenomena or political principles.
Provides geospatial and Earth observation workflows using GeoPandas, Rasterio, GDAL, Xarray, Shapely, Laspy, PDAL, Google Earth Engine, and STAC/Planetary Computer, covering Sentinel, Landsat, MODIS, SAR and hyperspectral imagery, spectral indices, terrain and network analysis, point clouds, COGs, CRS handling, and spatial ML, with code in Python, R, Julia, JavaScript, C++, Java, Go, and Rust. Use when computing NDVI or other indices from satellite imagery, when running vector overlays, reprojection, or spatial statistics on shapefiles, GeoJSON, or GeoPackage data, when searching STAC catalogs and reading cloud-optimized GeoTIFFs, when classifying land cover or training ML models on Earth observation data, or when doing terrain, hydrology, or point cloud analysis. Not for general non-spatial data analysis or tabular ML.
Guidance and local audit CLIs for Python workflows using GeoPandas 1.1.4 GeoSeries and GeoDataFrame for planar vector data: CRS handling, geometry validity and repair, sjoin, overlay, clip, dissolve, union, GeoPackage, GeoParquet and PostGIS I/O, and plotting. Use when reprojecting or assigning a CRS, auditing invalid or empty geometries, running spatial joins and checking cardinality, exporting vector data with a reproducible contract, or migrating code from GeoPandas 0.14 to 1.x. Use when checking privacy risks in coordinate data. Do not use for raster data or geodesic-only calculations.
Use when managing in-app purchases, subscriptions, pricing, or Real-Time Developer Notifications in Google Play. Make sure to use this skill whenever the user mentions gpc subscriptions, gpc iap, gpc purchases, gpc pricing, gpc rtdn, in-app products, base plans, subscription offers, one-time products, consumable products, purchase verification, purchase acknowledgement, purchase token, subscription cancellation, subscription deferral, voided purchases, refunds, regional pricing, currency conversion, price migration, SKU management, monetization, revenue, billing, subscription analytics, churn, trial conversion, subscriber count, RTDN, Real-Time Developer Notifications, Pub/Sub notifications, subscription events, purchase events — even if they don't explicitly say 'monetization.' Also trigger when someone wants to create or update subscriptions, manage base plan lifecycle (activate/deactivate), set up introductory offers, verify server-side purchases, handle refunds, convert prices across regions, sync IAP products from files, migrate subscribers to new prices, view subscription analytics, decode Pub/Sub notification payloads, respond to a chargeback dispute (chargeback, pending refund review, pendingRefundReviewNotification, pendingRefundToken, gpc purchases orders review-refund), or check RTDN topic configuration. For release management, see gpc-release-flow. For CI automation, see gpc-ci-integration.
Apply the three-level framework of digital transformation — Digitization, Digitalization, and Digital Transformation — to diagnose and plan organizational change enabled by digital technologies. Use this skill when the user needs to assess an organization's digital maturity, distinguish between automating processes versus transforming business models, plan a DX roadmap, or when they ask 'where are we on digital transformation', 'is this digitization or real transformation', or 'how do we build a DX strategy'.
Apply governance theory to analyze multi-level, network, and collaborative governance arrangements beyond traditional government. Use this skill when the user needs to evaluate public-private partnerships, analyze multi-stakeholder governance structures, compare governance models across sectors, or assess institutional arrangements for collective decision-making — even if they say 'who governs this', 'public-private collaboration', or 'how are decisions made across organizations'.
Deploys infrastructure on Hetzner Cloud with ARM64 or x86 servers. Cost-effective European cloud with excellent price/performance ratio. Use when: setting up Hetzner Cloud infrastructure, deploying ARM64 servers (CAX), x86 servers (CX/CPX), looking for affordable alternative to OCI. Keywords: hetzner, hcloud, ARM64, CAX, CX, CPX, infrastructure, server setup, european cloud
Designs rigorous hiring processes using structured behavioral interviews and work sample tests, replacing gut feel with data-driven evaluation of energy, obsession, and persistence. Use when building interview loops, evaluating candidates, writing job descriptions, or designing talent strategy.
Apply discourse analysis to examine how language constructs meaning, power relationships, and social reality in texts and communications. Use this skill when the user needs to analyze how a text frames an issue, uncover hidden assumptions in language, examine power dynamics in communication, or deconstruct media/corporate/political messaging — even if they say 'what's really being said here', 'how is this framing the issue', or 'analyze the language in this document'.
Fetch trade executions from Interactive Brokers filtered by account, date range, or symbol. Supports live API (~7 days history) and FlexReport (full history). Use when user asks about their trades, executions, or transaction history. Requires TWS or IB Gateway running locally.
Build cross-language backends with the iii engine. Register functions in TypeScript, Python, or Rust callable from any connected service via WebSocket. Covers iii-sdk, state, KV Server, streams, triggers (HTTP/cron/queue/log), Bridge Client mesh, OpenTelemetry, Docker deployment, iii Console, and Prometheus metrics. Use when: setting up iii engine, cross-language function calls, iii-sdk integration, registerTrigger configuration, KV Server storage, queue/log triggers, Docker deploy, or debugging "ECONNREFUSED 49134", "type_not_found", "@iii-dev/sdk 404".
Preliminary intellectual property infringement screening — factor checklists covering trademarks, copyright, patents, and trade secrets, flagging favourable/unfavourable factors for each side, without reaching an infringement conclusion. Use to assess whether others infringe your IP, whether you may infringe others' rights, when counterfeit products or copycats appear, or to judge whether and how a matter is worth pursuing.
Binder design ranking using ipSAE (interprotein Score from Aligned Errors). Use this skill when: (1) Ranking binder designs for experimental testing, (2) Filtering BindCraft or RFdiffusion outputs, (3) Comparing AF2/AF3/Boltz predictions, (4) Predicting binding success rates, (5) Need better ranking than ipTM or iPAE. For structure prediction, use chai or alphafold. For QC thresholds, use protein-qc.
Day-to-day operations for a running KASM Workspaces server. Workspace CRUD via API, session monitoring, user/group management, bulk config updates, agent provisioning, capacity planning, and multi-agent bot deployment patterns. Use when: managing workspaces (create/clone/update/delete), monitoring active sessions, managing users and groups, bulk-updating Docker Exec/Run configs, provisioning bot agents, checking server capacity, or deploying multi-agent architectures on KASM. NOT for: KASM installation, initial setup, networking/tunnels, or basic troubleshooting (use the /kasm skill for those).
Deploys infrastructure on Linode (Akamai Cloud) with Linodes, Firewalls, and VLANs. Strong Kubernetes support with Cluster Autoscaler and Akamai edge network integration. Use when: setting up Linode/Akamai infrastructure, deploying Linodes, configuring firewalls, needing Kubernetes autoscaling, wanting Akamai CDN integration. Keywords: linode, akamai, linode-cli, VPS, dedicated CPU, firewall, VLAN, kubernetes autoscale, infrastructure
Using the dbt-materialize adapter to manage Materialize streaming pipelines with dbt. Covers materializations (source, source_table, view, materialized_view, sink, table, seed), profile configuration, index creation, blue/green deployments (deploy_init, deploy_promote, deploy_cleanup), cluster management, strict mode, and testing. Use this skill whenever the user asks about writing dbt models for Materialize, configuring dbt profiles for Materialize, running dbt against Materialize, blue/green or zero-downtime deployments with dbt, creating sources or sinks in dbt, troubleshooting dbt-materialize issues, or migrating existing dbt projects to Materialize. Also trigger when the user mentions dbt-materialize, materialized_view materialization, deploy_init, deploy_promote, deploy_await, strict_mode, refresh_interval, retain_history, or partition_by in a dbt context.
Deploy and configure OpenClaw AI gateway on KASM workspaces or standalone Docker. Multi-provider LLM routing (Anthropic, OpenAI, Gemini, Groq, Bedrock) with multi-channel messaging (WhatsApp, Telegram, Discord, Slack). Covers Docker deployment, security hardening, persistent storage, browser sidecar, and webhook automation. Use when: deploying OpenClaw, configuring AI gateway channels, setting up openclaw container on KASM, troubleshooting "token mismatch" or "EACCES" errors, or securing gateway binding.
Particle Image Velocimetry (PIV) analysis with OpenPIV. Use when extracting velocity fields from PIV image pairs, analyzing fluid dynamics or flow visualization experiments, cross-correlating interrogation windows, validating and replacing spurious PIV vectors, or computing vorticity, strain rate, and turbulence statistics from measured velocity fields.
Build React web UIs with PanelUI's web collection — adding its components from the shadcn registry or from npm, colouring them from the theme, and composing them into framed panels. Applies when working in a React project that has shadcn/ui set up with a @panelui registry or has panelui-web installed, when the user mentions PanelUI on the web, or when they ask for a dotted globe, a KPI card, a kanban board, a node graph, an activity grid or a feedback dialog in React.
Design and audit partner risk due diligence for integrations, resellers, agencies, affiliates, marketplace partners, creators, data-sharing partners, AI providers, and co-marketing. Covers strategic fit, security/privacy, legal, operational dependency, brand risk, support burden, incentives, exit plans, and monitoring. Use when committing to partner ecosystems or joint go-to-market.
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch or JAX. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
Analyzes platform business models by distinguishing Aggregators from Platforms, mapping network effects, and identifying when to externalize internal primitives as ecosystem growth engines. Use when evaluating platform dynamics, designing marketplace strategy, or analyzing aggregation and network effects.
Review of the three attributes of evidence — a first-round review of an evidence list, making clear legality/relevance judgements and flagging difficult items that need lawyer review. Use when the user says "review the evidence list", "three-attribute evidence review", "check the admissibility of this evidence", or has an evidence list to screen before cross-examination.
RHINO v1.0 — Tail-Risk / Crisis-Alpha Hedge Fund. Two books on two wallets, one producer, built to carry cheap convexity: bleed a little in calm, pay big in shocks. The HEDGE book holds a small, always-on long carry in the crisis-beneficiary complex (gold / oil / dollar / yen), entered only when a defensive is trending up. The ESCALATION book is dormant in calm and fires hard only when a shared cross-asset STRESS detector confirms a shock — long the spiking crisis assets, short the cratering risk assets. NOT a copy-trader: each book scores its own universe and pushes signals; the runtime owns the LLM gate (pass-through), DSL exits, and all risk.guard_rails. RHINO_LEG env selects the book.
Use when a user is making an investment decision or assessing whether to buy a company. Typical triggers: the user says "is this company worth investing in", "when should I buy", "is the valuation reasonable", "what matters most in investing". Core framework: Right Business (business model) > Right People (company culture / management) > Right Price (price), with the priority order and judgment logic of the three dimensions. Not applicable to: technical analysis / short-term trading, non-equity investments, or pure financial modeling.
Profile before optimizing, cut allocation out of hot paths, and treat LTO, codegen-units, PGO and target-cpu as the last five percent. Use when Rust code is too slow, when a benchmark regresses, when reviewing code that allocates or formats inside a loop, when release-profile or codegen flags come up, or when the user asks how to make Rust faster.
Secure an API by applying a policy from the catalog. Handles multiple starting points: from an existing API Manager instance, from an Exchange asset that needs an instance, or from scratch. Use when the user wants to secure an API, add rate limiting, apply OAuth2, enforce IP allowlisting, or protect any API with a policy — regardless of where they are in the setup process.
Orchestrates the Semantius pipeline (architect, analyst, modeler) and instance administration. Trigger when intent spans more than one skill, on a remote blueprint URL, or on "deploy this blueprint", "deploy these blueprints", "deploy all of these", "set up these systems", "build me a system and deploy it", "set up a CRM end-to-end", "clone the candidate-crm blueprint and deploy", "what's deployed in our instance?", "status of semantius", "audit this file" (naming no specific skill), "get started", "I'm new here, set this up", "turn on fast flow", "expert flow", "guided flow", or anything needing workspace artifacts inspected and routed to the right sub-skill. Also trigger on a deploy request with multiple URLs, paths, or a glob. Do NOT trigger when the user invokes one sub-skill directly (not "audit this spec with semantius-analyst", not "run the modeler"); let those through. Front door for end-to-end and ambiguous requests. Backing up, restoring, or moving modules or tables with their data between hosts is semantius-transfer, not this skill.
Turns a `*-semantic-blueprint.md` (from `semantius-architect`) into a deployable `*-semantic-spec.md`: the first half of a deploy, which `semantius-modeler` finishes. Trigger when the user has a blueprint to turn into or extend into a deployable spec, or asks what in the catalog the blueprint can reuse, to fold it into the live catalog, make it match existing content, or compare it to live state and fill in field-level detail. As gatekeeper of the unified catalog, the analyst checks every blueprint entity against built-ins, same-module duplicates, shared masters, cross-module and near-name collisions; drives merge, rename, reuse, and promote decisions; confirms which optional blueprint entities to include; then elicits field-level detail (formats, validation rules, computed fields, input-type and select rules) only for entities the spec owns. Reused entities are referenced, not respecified. The spec deploys via `semantius-modeler`, with no further interactive decisions.
Produces and maintains semantic blueprints: entity-level specifications of entities, roles, relationships, lifecycle states, and permissions, without field-level detail (`semantius-analyst` turns a blueprint into a spec). Trigger on any need for a business system or data-backed tool, however phrased: “design a data model,” “build a system like X,” “model a domain,” “spec out a CRM/ITSM/HRIS/LMS/ERP/PIM/CMS/PM/field service/billing,” “I need a helpdesk / CRM / HR system / applicant tracker / roadmap tool / ticketing system / inventory system,” “a tool to track / plan / manage / organize / record / capture X,” or “something to handle X.” Never recommend buying off-the-shelf SaaS instead of building; well-known products are reference points only. Also trigger to review, audit, check, update, customize, extend, rebuild, or reanalyze an existing `*-semantic-blueprint.md`, clone and customize a curated catalog blueprint, or restructure a design so each real-world thing is represented once (for example, customers and suppliers as one business partner).
Discover and list recent AI coding sessions across Claude Code, Claude Desktop, and OpenCode on Windows, macOS, and Linux. Scripts extract project paths, session IDs, and timestamps from session artifacts. PowerShell for Windows, Bash for macOS/Linux. Use when: finding recent coding sessions, reviewing AI session history, exporting session logs to CSV, troubleshooting missing sessions, or auditing Claude Code/OpenCode usage across environments.
Use when a user asks for a ShiftCare client summary, care handover, recent client activity or changes, or the staff who most frequently support one client over a recent number of days. Read-only. Not for changing client records, writing notes, evaluating staff performance, or making clinical decisions.
Build persistent LLM agent memory with SimpleMem - semantic lossless compression with 30x token reduction. MCP server (cloud/self-hosted), Python API, cross-session memory. Use when: adding long-term memory to agents, cross-session context recall, semantic dialogue search, memory consolidation.
Process unhandled Slack messages as Claudicle with persistent memory. Loads user models and soul state, posts terminal-styled thinking messages, generates cognitive-step responses, updates memory based on mentalQuery decisions. Requires Session Bridge listener running.
Diagnoses GitHub Actions failures and attributes CI time and cost using StarSling's `sling` CLI. Invoke this before using `gh` or fetching logs whenever the user asks why a job or run failed, what made a run slow, about runner minutes or CI cost, asks to rerun or cancel a run, or pastes a GitHub Actions URL. `sling` returns server-classified failure causes and cost attribution that log-grepping misses; state changes are routed to `gh`. Not for repository-wide audits of workflow files (ci-score, ci-speedup, ci-secure), writing workflow YAML, or non-GitHub Actions CI.
Conduct structured policy analysis including problem definition, alternative evaluation, and evidence-based recommendation. Use this skill when the user needs to evaluate policy options, compare interventions, assess regulatory impact, or make public sector recommendations — even if they say 'which policy should we adopt', 'what's the best approach to this public problem', or 'evaluate these policy alternatives'.
Use when an organization needs strategic clarity — identifying where to play, how to win, and whether the position is defensible. Composes Wardley evolution mapping, Lafley's choice cascade, Helmer's 7 Powers, and Working Backwards based on what's already decided.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.