33 KiB
External AI agent skills — ict-research-consultant
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 |
|---|---|---|---|
docx |
core | 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 |
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 |
mcp-builder |
adjacent | 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 |
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 |
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 |
pptx |
adjacent | Use this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx or .potx file (even … | 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 |
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 |
core | 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 |
executing-plans |
adjacent | Use when you have a written implementation plan to execute in a separate session with review checkpoints | 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 |
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 |
test-driven-development |
adjacent | Use when implementing any feature or bugfix, before writing implementation code | source |
writing-plans |
adjacent | Use when you have a spec or requirements for a multi-step task, before touching code | source |
dispatching-parallel-agents |
adjacent | Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies | source |
systematic-debugging |
adjacent | Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes | 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 |
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 |
|---|---|---|---|
prompt-engineering-patterns |
core | This skill should be used when the user asks to "optimize a prompt", "improve prompt performance", "design a prompt template", "write better prompts", "debug prompt issues", "use chain-of-thought", "structured prompting", "few-shot … | 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 |
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 |
uv-package-manager |
core | Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python development workflows with uv. | source |
debugging-strategies |
core | Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior. | source |
code-documentation-code-reviewer (agent) |
adjacent | Elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. Masters static analysis tools, security scanning, and configuration review with … | source |
comprehensive-review-architect-review (agent) |
adjacent | 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 |
dbt-transformation-patterns |
adjacent | Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best … | source |
python-code-style |
adjacent | Python code style, linting, formatting, naming conventions, and documentation standards. Use when writing new code, reviewing style, configuring linters, writing docstrings, or establishing project standards. | source |
code-documentation-tutorial-engineer (agent) |
adjacent | Creates step-by-step tutorials and educational content from code. Transforms complex concepts into progressive learning experiences with hands-on examples. Use PROACTIVELY for onboarding guides, feature tutorials, or concept explanations. | source |
code-review-excellence |
adjacent | Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers. | source |
debugging-toolkit-dx-optimizer (agent) |
adjacent | Developer Experience specialist. Improves tooling, setup, and workflows. Use PROACTIVELY when setting up new projects, after team feedback, or when development friction is noticed. | source |
Source: ConardLi/garden-skills
- Repository: https://github.com/ConardLi/garden-skills (commit
fbd6453, retrieved 2026-07-14) - License: MIT
| Skill | Tier | What it adds | Upstream |
|---|---|---|---|
kb-retriever |
adjacent | 面向本地知识库目录的检索和问答助手。核心流程:(1)分层索引导航 (2)遇到PDF/Excel时必须先读取references学习处理方法 (3)处理文件后再检索。按文件类型组合使用 grep、Read、pdfplumber、pandas 进行渐进式检索,避免整文件加载。用户问题涉及"从知识库目录回答问题/检索信息/查资料"时使用。 | 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 |
|---|---|---|---|
cloud-sql-basics |
core | 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 |
bigquery-basics |
adjacent | 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 |
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 |
datalineage-bigquery-asset-impact-analysis |
adjacent | 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 |
google-analytics-data-api-basics |
adjacent | 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 |
agent-platform-eval-flywheel |
adjacent | 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 |
google-analytics-admin-api-basics |
adjacent | 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 |
agent-platform-alert-configuration |
adjacent | 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 |
alloydb-basics |
adjacent | Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB model context protocol (MCP) tools for automated database operations. | source |
bigtable-basics |
adjacent | 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 |
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 |
google-cloud-networking-observability |
adjacent | 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 |
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-subworkflows |
core | Build reusable, composable n8n sub-workflows. Use when extracting shared logic, building anything multi-step or reused across workflows, or any workflow over ~10 nodes — and whenever the user mentions sub-workflows, Execute Workflow, … | source |
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-multi-instance |
adjacent | 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-code-python |
adjacent | 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-self-hosting |
adjacent | 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-binary-and-data |
adjacent | Handle files and binary data in n8n correctly. Use when working with files, images, PDFs, attachments, uploads or downloads, base64, vision/multimodal input, or when an AI agent needs a file as tool input or output — and whenever the user … | 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-code-javascript |
adjacent | 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-tool |
adjacent | 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-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 |
n8n-validation-expert |
adjacent | Interpret validation errors and guide fixing them. Use when encountering validation errors, validation warnings, false positives, operator structure issues, or need help understanding validation results. Also use when asking about … | source |
n8n-workflow-patterns |
adjacent | 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 |
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-rl-auto-research |
core | Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring RL or NeMo-gym runs, launching … | source |
nemoclaw-user-guide |
core | Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. … | source |
data-designer |
core | Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline. | source |
nemo-data-designer-plugin |
core | Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline. | source |
nemo-rl-brev-etiquette |
adjacent | Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets. Use when running nemo-rl-auto-research campaigns, … | source |
nemo-rl-session-memory |
adjacent | Manage durable working-session memory for coding agents. Use when a user asks to preserve or recover agent context across disconnects, VS Code restarts, long-running work, handoffs, or any session where important state should be written … | source |
hsb-app |
adjacent | 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 |
vss-manage-alerts |
adjacent | Use for VSS alert workflows — real-time monitoring, Alert-Bridge subscriptions, Slack notifications, incident queries, camera onboarding. Not for non-alert analytics. | source |
cupynumeric-migration-readiness |
adjacent | Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer … | source |
holoscan-install-source |
adjacent | Build Holoscan SDK from source via the in-tree ./run script. Use only when published packages don't meet the user's needs. | source |
nemo-retriever |
adjacent | Use when the user wants to search, query, extract, transcribe, describe, quote, filter, or aggregate across documents — PDFs, scanned forms / images (.jpg .png .tiff), Office (.docx .pptx), text (.html .txt), audio (.mp3 … |
source |
physical-ai-infrastructure-setup-and-resilient-scaling |
adjacent | 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 |
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 |
|---|---|---|---|
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 |
adjacent | 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 |
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 |
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 |
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 |
dummy-dataset |
adjacent | 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 |
adjacent | 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 |
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 |
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-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-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-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-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-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-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-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 |
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 |