Context7

Optional AI and ML engineering specialization from model training to serving.

7 skills

Reproducible data, training, tracking, and experiment recipes.

4 skills

Ingestion, retrieval, provenance, and durable research memory.

4 skills

Evidence-grounded synthesis, papers, plots, and conference talks.

4 skills

Evaluation design, rigor review, and benchmark execution.

3 skills

Persist and recall cross-session memory and curated knowledge-vault references for your familiars.

2 skills

Harness-native ECC operator layer - 71 agents, 314 skills, 98 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses

200 skills · 98 commands · 71 agents · 1 MCP server · hooks

DeepSeek model harness with focused agents, commands, and skills for swarm-backed reasoning workflows

2 skills · 1 command · 1 agent

Turn any domain folder of skills into a bounded agentic loop: a manifest builder inventories a domain's skills/tools/checks, a goal compiler turns a goal into a verifiable task plan (refusing vague goals with forcing questions), and a JSON-backed loop controller drives execute->verify->close with retry caps, controller-run verification (no verification theater), human escalation on exhausted budgets, and a close gate that refuses while any task is unverified or unwaived. Ships 3 stdlib Python tools, 18 committed per-domain harness manifests + JSON schema, 3 references citing the 2024-2026 agent-harness canon (Anthropic long-running harnesses, verifier's law, SWE-agent, Ralph loop, Cognition), harness-runner agent + /cs-harness command. Use when an agent or subagent should pick up a goal, define its tasks, complete and verify them, and close the loop.

1 skill · 1 command · 1 agent

Direct CDP browser control for agents: coordinate clicks, screenshots, persistent Python session, local Chrome or Browser Use cloud. Ships skills only; the `browser-harness` CLI is a one-time install prerequisite.

3 skills

Comprehensive context engineering and harness engineering skills for production-grade AI agent systems: fundamentals, degradation patterns, compression, optimization, self-managed context (model-controlled live context editing), multi-agent coordination, long-horizon prompting (pseudo-formal task briefs for long-running and parallel agents), memory systems, tool design, filesystem context, hosted agents, evaluation, autonomous harnesses, self-improvement loops (RSI, meta-harnesses, evolutionary scaffold search), latent briefing (KV cache sharing between agents), project development, and cognitive architecture. Ships with a researcher operating system (rubrics, mechanism registry, claim provenance, run state machine, adversarial benchmarks, continuous loop).

19 skills

에이전트 팀 & 스킬 아키텍트 v2. 도메인/프로젝트에 맞는 하네스를 구성하고, 전문 에이전트를 정의하며, 에이전트가 사용할 스킬을 생성하는 메타 스킬. 워크플로우 오케스트레이션 · 퍼시스턴트 에이전트 협업 · 서브에이전트 위임 3중 실행 모드 지원.

2 skills