32 KiB
External AI agent skills — ict-system-architect
Proven, publicly available AI agent skills mapped to this occupation. Nothing is copied from the sources: every entry is a name, a one-line summary and a link to the upstream skill package. Each section names its source repository, commit, license and retrieval date.
Tiers: core = the skill directly exercises a top market hard
skill, tool or method (from gated job-ad evidence) or an essential
ESCO competence of this occupation; adjacent =
plausibly useful, secondary. Entries are capped at 12 per source
and 80 in total per occupation (core first,
strongest matches survive); everything beyond the caps is excluded
and logged in the pipeline audit trail, not in this package.
Matched deterministically (ISCO group + title/competence keywords,
tiered against market evidence + ESCO essentials) by
pipeline/p5_enrich_ai_skills.py on 2026-07-14.
Source: anthropics/skills
- Repository: https://github.com/anthropics/skills (commit
f6656c1, retrieved 2026-07-14) - License: Apache-2.0; the document skills (docx/pdf/pptx/xlsx) are source-available — see the LICENSE.txt in the upstream skill folder
| Skill | Tier | What it adds | Upstream |
|---|---|---|---|
mcp-builder |
core | Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python … | source |
claude-api |
adjacent | 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 … | source |
webapp-testing |
adjacent | Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs. | source |
docx |
adjacent | Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to … | source |
skill-creator |
adjacent | Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with … | source |
pdf |
adjacent | Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating … | source |
Source: obra/superpowers
- Repository: https://github.com/obra/superpowers (commit
d884ae0, retrieved 2026-07-14) - License: MIT (c) Jesse Vincent
| Skill | Tier | What it adds | Upstream |
|---|---|---|---|
brainstorming |
adjacent | You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation. | source |
finishing-a-development-branch |
adjacent | Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup | source |
test-driven-development |
adjacent | Use when implementing any feature or bugfix, before writing implementation code | source |
receiving-code-review |
adjacent | Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation | source |
requesting-code-review |
adjacent | Use when completing tasks, implementing major features, or before merging to verify work meets requirements | source |
subagent-driven-development |
adjacent | Use when executing implementation plans with independent tasks in the current session | source |
writing-plans |
adjacent | Use when you have a spec or requirements for a multi-step task, before touching code | source |
using-git-worktrees |
adjacent | Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback | source |
dispatching-parallel-agents |
adjacent | Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies | source |
executing-plans |
adjacent | Use when you have a written implementation plan to execute in a separate session with review checkpoints | source |
systematic-debugging |
adjacent | Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes | source |
verification-before-completion |
adjacent | Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always | source |
Source: wshobson/agents
- Repository: https://github.com/wshobson/agents (commit
6fd3247, retrieved 2026-07-14) - License: MIT (c) Seth Hobson
| Skill | Tier | What it adds | Upstream |
|---|---|---|---|
code-documentation-docs-architect (agent) |
core | Creates comprehensive technical documentation from existing codebases. Analyzes architecture, design patterns, and implementation details to produce long-form technical manuals and ebooks. Use PROACTIVELY for system documentation, … | source |
comprehensive-review-architect-review (agent) |
core | Master software architect specializing in modern architecture patterns, clean architecture, microservices, event-driven systems, and DDD. Reviews system designs and code changes for architectural integrity, scalability, and … | source |
python-project-structure |
core | Python project organization, module architecture, and public API design. Use when setting up new projects, organizing modules, defining public interfaces with all, or planning directory layouts. | source |
recsys-pipeline-architect |
core | Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced X For You algorithm. Use when building any system that picks … | source |
langchain-architecture |
core | Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows. | source |
bats-testing-patterns |
core | Master Bash Automated Testing System (Bats) for comprehensive shell script testing. Use when writing tests for shell scripts, CI/CD pipelines, or requiring test-driven development of shell utilities. | source |
full-stack-orchestration-performance-engineer (agent) |
core | Expert performance engineer specializing in modern observability, application optimization, and scalable system performance. Masters OpenTelemetry, distributed tracing, load testing, multi-tier caching, Core Web Vitals, and performance … | source |
bash-defensive-patterns |
core | Master defensive Bash programming techniques for production-grade scripts. Use when writing robust shell scripts, CI/CD pipelines, or system utilities requiring fault tolerance and safety. | source |
c4-component (agent) |
core | Expert C4 Component-level documentation specialist. Synthesizes C4 Code-level documentation into Component-level architecture, defining component boundaries, interfaces, and relationships. Creates component diagrams and documentation. Use … | source |
c4-container (agent) |
core | Expert C4 Container-level documentation specialist. Synthesizes Component-level documentation into Container-level architecture, mapping components to deployment units, documenting container interfaces as APIs, and creating container … | source |
c4-context (agent) |
core | Expert C4 Context-level documentation specialist. Creates high-level system context diagrams, documents personas, user journeys, system features, and external dependencies. Synthesizes container and component documentation with system … | source |
async-python-patterns |
core | Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations. | source |
Source: google/skills
- Repository: https://github.com/google/skills (commit
b15f327, retrieved 2026-07-14) - License: Apache-2.0
| Skill | Tier | What it adds | Upstream |
|---|---|---|---|
agent-platform-alert-configuration |
core | Configures best-practice alerting policies for Google Cloud Vertex AI / Agent Platform agents on Agent Runtime. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, and quality metrics … | source |
agent-platform-eval-flywheel |
core | Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing … | source |
alloydb-basics |
core | Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB model context protocol (MCP) tools for automated database operations. | source |
bigquery-basics |
core | Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis. | source |
google-analytics-admin-api-basics |
core | Manages Google Analytics account and property settings, enables the Analytics Admin API via the Cloud CLI, lists accounts and properties, and manages data streams, custom dimensions, conversion events, and integrations. Use when you need … | source |
google-analytics-data-api-basics |
core | Manages Google Analytics reporting data, enables the Analytics Data API via the Cloud CLI, and creates reports using the Google Analytics Data API (v1beta). Use when you need to interact with Google Analytics properties, run customized … | source |
bigtable-basics |
core | Assists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for … | source |
google-cloud-networking-observability |
core | Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs (including cost estimation), NAT, firewall, or threat logs, querying latency and throughput metrics, or running … | source |
datalineage-bigquery-asset-impact-analysis |
core | Analyzes the downstream impact (blast radius) when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact … | source |
cloud-sql-basics |
adjacent | This file generates or explains Cloud SQL resources. Use this file when the user asks to create a Cloud SQL instance or database for MySQL, PostgreSQL, or SQL Server. Cloud SQL manages third-party MySQL, PostgreSQL, and SQL Server … | source |
gke-observability |
adjacent | Configures GKE observability, including Cloud Logging, Cloud Monitoring, and managed Prometheus. Use when configuring GKE monitoring, setting up GKE logging, or configuring Prometheus metrics collection. Don't use to configure local … | source |
bigquery-ai-ml |
adjacent | Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, detect outliers, find key drivers, or leverage generative AI … | source |
Source: czlonkowski/n8n-skills
- Repository: https://github.com/czlonkowski/n8n-skills (commit
9ea3aa5, retrieved 2026-07-14) - License: MIT
| Skill | Tier | What it adds | Upstream |
|---|---|---|---|
n8n-agents |
core | Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool … | source |
n8n-workflow-patterns |
core | Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API … | source |
n8n-code-javascript |
core | Write JavaScript code in n8n Code nodes. Use when writing JavaScript in n8n, using $input/$json/$node syntax, making HTTP requests with this.helpers / the $helpers global, working with dates using DateTime, troubleshooting Code node … | source |
n8n-code-python |
core | Write Python code in n8n Code nodes. Use when writing Python in n8n, using _input/_json/_node syntax, working with standard library, or need to understand Python limitations in n8n Code nodes. Use this skill when the user specifically … | source |
n8n-code-tool |
core | Write JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the workflow Code node. Use when building a Code Tool attached to an AI Agent, writing code that an LLM will … | source |
n8n-expression-syntax |
core | Validate n8n expression syntax and fix common errors. Use when writing n8n expressions, using {{}} syntax, accessing $json/$node variables, troubleshooting expression errors, mapping data between nodes, or referencing webhook data in … | source |
n8n-multi-instance |
core | Use when an n8n-mcp account targets more than one n8n instance — i.e. the n8n_instances tool is available, the user mentions multiple n8n instances or environments (prod vs staging, several teams or clients), a workflow / datatable / … |
source |
n8n-self-hosting |
core | Deploy a production self-hosted n8n end-to-end to a fresh Linux VM over SSH, using Docker Compose behind a Caddy reverse proxy with automatic HTTPS. Use whenever the user wants to self-host, install, set up, provision, or deploy n8n on … | source |
n8n-mcp-tools-expert |
adjacent | Expert guide for using n8n-mcp MCP tools effectively. Use when searching for nodes, validating configurations, accessing templates, managing workflows, managing credentials, auditing instance security, or using any n8n-mcp tool. Provides … | source |
using-n8n-mcp-skills |
adjacent | Use when building, editing, validating, testing, or debugging an n8n workflow through the n8n-mcp MCP server — designing a flow, configuring a node, writing an expression or Code node, wiring credentials, or fixing one that misbehaves. The … | source |
n8n-error-handling |
adjacent | Wire n8n error handling so failures are loud, structured, and recoverable. Use when building any webhook/API workflow, a scheduled or unattended workflow, or any path where a silent failure would drop user-visible work — and whenever the … | source |
n8n-node-configuration |
adjacent | Operation-aware node configuration guidance. Use when configuring nodes, understanding property dependencies, determining required fields, choosing between get_node detail levels, or learning common configuration patterns by node type. … | source |
Source: NVIDIA/skills
- Repository: https://github.com/NVIDIA/skills (commit
153b14b, retrieved 2026-07-14) - License: CC-BY-4.0 (skills/docs), Apache-2.0 (code)
| Skill | Tier | What it adds | Upstream |
|---|---|---|---|
nemo-automodel-model-onboarding |
core | Guide for onboarding new model architectures into NeMo AutoModel, including architecture discovery, implementation patterns, registration, and validation. | source |
tilegym-converting-cutile-to-julia |
core | Converts cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents. Handles kernel syntax translation, 0-indexed to 1-indexed conversion, broadcasting differences, memory layout (row-major to column-major), type system mapping, … | source |
deepstream-dev |
core | NVIDIA DeepStream SDK 9.0 development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker … | source |
tao-train-segformer |
core | SegFormer for semantic segmentation. Lightweight transformer-based architecture with hierarchical feature extraction, efficient for real-time segmentation tasks. Use when training, evaluating, exporting, quantizing, or running inference … | source |
tao-train-oneformer |
core | OneFormer for universal image segmentation. Unifies panoptic, instance, and semantic segmentation with a single architecture using task-conditioned queries. Use when training, evaluating, exporting, quantizing, or running inference for a … | source |
hsb-app |
core | Discover and run Holoscan Sensor Bridge example applications on a connected devkit. Filters available apps by the user's platform, HSB software version, board type, and sensors. Supports timed execution, failure analysis, code-edit … | source |
cupynumeric-install |
core | Install and verify cuPyNumeric for Python — requirements, commands, verification. Source builds are out of scope. | source |
physical-ai-infrastructure-setup-and-resilient-scaling |
core | Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO … | source |
tao-run-platform |
core | TAO Execution SDK for submitting and monitoring GPU training jobs on supported platforms (Brev, SLURM, local Docker, Kubernetes). Use when the user wants to run TAO jobs through the SDK, get job tracking, S3 I/O wrapping, multi-node … | source |
tilegym-cutile-python |
core | Expert cuTile programming assistant. Write high-performance GPU kernels using cuTile's tile-based programming model with proper validation and optimization. Supports deep agent orchestration for complex multi-kernel tasks. | source |
amc-setup-calibration-stack |
core | Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API … | source |
cuopt-numerical-optimization-api |
core | LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface. | source |
Source: phuryn/pm-skills
- Repository: https://github.com/phuryn/pm-skills (commit
18468a9, retrieved 2026-07-14) - License: MIT
| Skill | Tier | What it adds | Upstream |
|---|---|---|---|
dummy-dataset |
core | Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and … | source |
lean-canvas |
core | Generate a Lean Canvas with problem, solution, metrics, cost structure, UVP, unfair advantage, channels, segments, and revenue. Use when exploring a lean startup canvas, testing a business hypothesis, or modeling a new venture. | source |
ab-test-analysis |
core | Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split … | source |
cohort-analysis |
core | Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or … | source |
metrics-dashboard |
adjacent | Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan. | source |
north-star-metric |
adjacent | Define a North Star Metric and 3-5 supporting input metrics that form a metrics constellation. Classify the business game (Attention, Transaction, Productivity) and validate against 7 criteria for an effective North Star. Use when choosing … | source |
sql-queries |
adjacent | Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring … | source |
gtm-strategy |
adjacent | Create a go-to-market strategy covering marketing channels, messaging, success metrics, and launch timeline. Use when planning a product launch, creating a GTM plan from scratch, or defining a launch strategy for a new market. | source |
product-strategy |
adjacent | Create a comprehensive product strategy using the 9-section Product Strategy Canvas — vision, segments, costs, value propositions, trade-offs, metrics, growth, capabilities, and defensibility. Use when building a product strategy, creating … | source |
Source: veniceai/skills
- Repository: https://github.com/veniceai/skills (commit
de089fa, retrieved 2026-07-14) - License: MIT
| Skill | Tier | What it adds | Upstream |
|---|---|---|---|
venice-characters |
adjacent | Discover and use Venice public characters (persona-driven system prompts with a bound model). Covers GET /characters (search/filter/sort), /characters/{slug}, /characters/{slug}/reviews, the Character schema, and how to apply a character … | source |
venice-api-keys |
adjacent | Manage Venice API keys. Covers GET/POST/PATCH/DELETE /api_keys, GET /api_keys/{id}, GET /api_keys/rate_limits, GET /api_keys/rate_limits/log, the two-step /api_keys/generate_web3_key wallet flow, INFERENCE vs ADMIN key types, and per-key … | source |
venice-api-overview |
adjacent | High-level map of the Venice.ai API - base URL, authentication modes, endpoint categories, response headers, pricing model, error shape, and versioning. Load this first when starting any Venice integration. | source |
venice-errors |
adjacent | Handle Venice API errors correctly. Covers the StandardError / DetailedError / ContentViolationError / X402InferencePaymentRequired body shapes, every meaningful status code (400, 401, 402, 403, 415, 422, 429, 500, 503, 504), the 402 … | source |
venice-responses |
adjacent | Use Venice's Alpha POST /responses endpoint - an OpenAI-compatible Responses API with typed output blocks (reasoning, message, function_call, web_search_call). Covers request shape, streaming, differences from /chat/completions, supported … | source |
venice-auth |
adjacent | Authenticate to the Venice API with a Bearer API key or with an x402 / SIWE wallet. Covers header formats, the SIWE message fields, TTL and nonce rules, the venice-x402-client SDK, and how to choose between the two modes. | source |
venice-embeddings |
adjacent | Call POST /embeddings on Venice. Covers request shape (input, model, encoding_format, dimensions, user), OpenAI compatibility, response compression (gzip/br), and practical usage for retrieval, clustering, and RAG. | source |
venice-image-edit |
adjacent | Transform existing images with Venice. Covers POST /image/edit (prompt-driven single-image edit), /image/multi-edit (compose 1-3 images), /image/upscale (2-4x upscale + enhance), and /image/background-remove. Accepts base64, file upload, … | source |
venice-image-generate |
adjacent | Generate images with Venice. Covers POST /image/generate (Venice-native), POST /images/generations (OpenAI-compatible), GET /image/styles (style presets), request fields (prompt, dimensions, cfg_scale, seed, variants, style_preset, … | source |