Browse 5,831 Fullstack & Backend Engineering — Agentic Skills

Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.

Measure performance baselines and detect regressions across browser Core Web Vitals (LCP, INP, CLS, page weight), API endpoint latency percentiles, and build/test feedback times, with before/after comparison stored in git-tracked .ecc/benchmarks JSON. Use when checking page speed, responding to 'it feels slow' reports, verifying launch performance targets, or comparing stack alternatives.

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs. Use when a task needs quick bioinformatics lookup across genomic reference databases with the gget CLI or Python package.

Evidence-first mailbox triage, drafting, send verification, and sent-mail-safe follow-up workflow for ECC. Use when the user wants to organize email, draft or send through the real mail surface, or prove what landed in Sent.

Split an RFC into a multi-agent execution DAG — decompose into work units with dependencies and acceptance tests, run research, plan, implement, test, and review per unit, then merge through a queue with re-based branches and final system verification. Use when a feature is too large for a single agent pass, orchestrating RFC-driven multi-agent execution, or managing merge queues across agent-built units.

Codified customs and trade compliance expertise — HS/HTS tariff classification with GRI rules, commercial invoices and entry documentation, Incoterms 2020, FTA qualification and duty optimization (USMCA, RCEP, FTZs, drawback), denied-party screening, and penalty mitigation across US, EU, UK, and APAC jurisdictions. Use when classifying goods, preparing import/export documentation, screening restricted parties, responding to customs audits or CF-28/penalty notices, or optimizing duties.

Browse and search the AgentAPI directory - a curated database of APIs designed for AI agents.

FastAPI best practices covering project structure, Pydantic v2 schemas, dependency injection, async handlers, authentication, authorization, transactional service layers, and testing with httpx and pytest. Use when building or reviewing FastAPI apps — Pydantic schemas, dependencies, async handlers, auth, or tests.

Django architecture patterns, REST API design with DRF, ORM best practices, caching, signals, middleware, and production-grade Django apps. Use when building or reviewing Django apps, DRF APIs, ORM queries, or caching.

PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading. Use when writing or reviewing PyTorch training loops, model architectures, or data loading, or when a run will not reproduce.

Translate and synchronize application JSON locale files using source-key usage, project terminology, and focused validation. Use when adding keys or languages, updating source copy, or reviewing missing and stale translations.

Prevent Ethereum hashing bugs in JavaScript and TypeScript. Node's sha3-256 is NIST SHA3, not Ethereum Keccak-256, and silently breaks selectors, signatures, storage slots, and address derivation. Use when hashing for Ethereum in JavaScript or TypeScript, or when a selector, signature, storage slot, or derived address is wrong.

Idiomatic Kotlin patterns, best practices, and conventions for building robust, efficient, and maintainable Kotlin applications with coroutines, null safety, and DSL builders. Use when writing or reviewing Kotlin code and idiomatic structure or null safety is in question.

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

Establish or maintain a business module's local agent instructions, domain-context links, and interface ownership when implementing a new module or changing its responsibilities. Reuse project-level rules and existing ECC workflows; ordinary bug fixes, styling, and read-only design discussions do not start module documentation setup.

Diagnostics-only BGP troubleshooting patterns for neighbor state, route exchange, prefix policy, AS path inspection, and safe evidence collection. Use when a BGP neighbor is down, routes are missing, or prefix policy and AS path need inspection.

Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination. Use when capturing lessons from a session, managing instincts, or promoting them into skills, commands, or agents.

Cisco IOS and IOS-XE review patterns for show commands, config hierarchy, wildcard masks, ACL placement, interface hygiene, and safe change-window verification. Use when reading, writing, or reviewing Cisco IOS / IOS-XE configuration or planning a change window.

Test-driven development workflow: write a failing test first, watch it fail, implement the smallest change to green, then refactor with 80%+ coverage across unit, integration, and E2E tests. Use when writing a new feature, fixing a bug, refactoring, or when told to write failing tests first.

Research-before-coding workflow: search npm/PyPI, MCP servers, skills, and GitHub for existing tools before writing custom code, then adopt, extend, or build. Launches the researcher agent for non-trivial needs. Use when starting a feature, adding a dependency or integration, or about to write a utility that may already exist.

Use up-to-date library and framework docs via Context7 MCP instead of training data. Activates for setup questions, API references, code examples, or when the user names a framework (e.g. React, Next.js, Prisma).

Multi-agent adversarial verification: two independent reviewers with the same rubric must both pass before output ships, with a fix-and-re-review convergence loop and human escalation cap. Use when gating publishing, production deploys, compliance or brand-sensitive content, or hallucination-prone claims before they ship.

Analyze draft prompts, detect intent and missing context, match ECC commands, skills, and agents, and output a ready-to-paste optimized prompt with diagnosis and rationale — advisory only, never executes the task. Use when the user says 'optimize prompt', 'improve my prompt', 'rewrite this prompt', 'help me prompt', 优化prompt, 改进prompt, 怎么写prompt, or 帮我优化这个指令; not for requests to optimize code or performance.

Auto-generate beautiful changelogs from git history, grouped by conventional commit types.