33건 · 관련도순
Manage Hindsight memory for coding sessions: retrieve past decisions, store documents and initiative plans, correct outdated information, and configure memory by project. It also explains automatic conversation capture and helps investigate memory configuration problems.
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Find which companion skill to use next and in what order. This guide maps ideas, prototypes, requirements, issues, implementation, and session handoffs into practical workflows based on the current development situation.
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Ask which skill or flow fits your situation. A router over the user-invoked skills in this repo.
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Designs coding-agent plugin systems. Use when adding plugin manifests, extension points, or reloadable integrations, or keeping plugin cache compatible across CLI upgrades.
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Designs approval and sandbox policy for local coding agents: permission modes, isolation, network and destructive-command guards. Use when setting approvals. Not cloud sandboxes.
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Designs coding-agent observability and evals. Use when measuring traces, replay, checkpoint lineage, tool grading, regression, or cost. Not for general LLM evals (ai-evals).
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Designs tool and slash-command runtimes for coding-agent CLIs: registries, deferred loading, tool search, dispatch, forks. Use when modeling tool pools. Not approvals or plugins.
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Designs session and task state for coding-agent runtimes: resume, checkpoints, compaction, cancellation, background and scheduled runs. Use when persisting agent work.
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Creates coding-agent definitions (review, test, refactor, team) for Claude Code, Codex, and Agent SDK. Use when writing a code-review or test subagent, not a runtime.
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Designs coding-agent settings and fleet CLI version control. Use when diagnosing precedence, managed policy, version pins, update kill switch, catalog budgets, or searchable notes.
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Designs user-facing surfaces of coding-agent runtimes: remote and bridge sessions, transports, reconnect, terminal REPL, input history. Use when building agent UIs.
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Designs provider runtimes for coding agents. Use when modeling model abstraction, streaming semantics, tool-call normalization, retries, or fallback routing.
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How to use the coding agents installed on this machine (claude, codex, droid, grok, vktr, hermes, opencode, copilot, pi) as plain sub-agents in orchestrated workflows: one headless invocation, one prompt in, one result out. Deliberately ignores each CLI's own orchestration features (droid --mission, codex multi_agent, hermes delegation/moa/kanban, grok --agents, opencode personas). The orchestrator is whatever session is running the skill; the CLIs are interchangeable workers. Use when the user asks "which agent should run this", "fan out workers", "run X headless", or when a lifeos skill (overnight, timeboxed-iterating, workgraph, gauntlet, bughunt, dark-factory) needs to launch an agent CLI, or when a worker needs company context it should ask Viktor for (vktr).
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Post-train LLMs with PrimeIntellect's prime-rl (async RL, SFT, distillation, evals). Use this skill whenever the user mentions prime-rl, wants to write an rl.toml or sft.toml, run `uv run rl` / `uv run sft` / `uv run eval` / `uv run inference`, pick GRPO, MaxRL, OPD or ECHO, tune IPO loss, or monitor and resume a run. Also trigger for verifiers environments, Prime Intellect post-training, reverse-text, and pydantic-config TOML overrides.
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Build GenAI and agentic apps with genai-tk (tclatos/genai-tk), a YAML-driven IoC toolkit over LangChain, DeepAgents, DeerFlow, and Prefect. Use this skill whenever the user mentions genai-tk, genai_tk, `cli init`, `cli agents run`, `LangchainAgent`, `create_harness`, `get_llm`, `global_config()`, `model_id@provider`, `agents:`/`agent_defaults:` profiles, `RetrieverFactory`, `cli workflow`, or BAML extraction. Also trigger for app_conf.yaml, GENAITK_PROFILE, CodeAct, OpenSandbox, decision models, or "the toolkit".
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Fine-tune LLMs and VLMs using Unsloth on HF Jobs (Hugging Face on-demand cloud GPUs). Use when users want to fine-tune language models, train VLMs (Vision Language Models), do continued pretraining, domain adaptation, or run UV scripts on HF Jobs. Triggers on requests involving Unsloth training, HF Jobs GPU training, Qwen3-VL fine-tuning, Gemma VLM training, or LoRA fine-tuning on cloud GPUs.
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Plan, write, and maintain automated tests in any language, with worked examples in Python and JavaScript/TypeScript. Use this skill whenever the user wants to write or plan tests, apply TDD (red-green-refactor), structure unit tests with arrange-act-assert and given-when-then naming, choose between fakes, stubs, and mocks, set up dependency injection for testability, build integration tests with testcontainers, database seeding, or transaction rollback, write Playwright or Cypress end-to-end tests, use property-based testing with Hypothesis or fast-check, create pytest fixtures or factory_boy factories, review coverage or mutation testing, or triage flaky tests in CI. Also trigger for "test pyramid", "test trophy", "test isolation", "mocking", "snapshot tests", "golden files", "parametrized tests", "make the tests pass".
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Decompose systems into microservices and apply canonical distributed-systems patterns. Use this skill whenever the user wants to split a monolith into services, design service boundaries, choose between microservices and a modular monolith, implement sagas or the outbox pattern, set up CQRS or event sourcing, configure an API gateway or BFF, add resilience (circuit breakers, retries, timeouts), or implement distributed tracing with OpenTelemetry. Also trigger for distributed transactions, service discovery, event-driven architecture, contract testing with Pact, canary releases, and idempotent consumers.
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Build AI agents with LangChain v1 and LangGraph in Python. Use this skill whenever the user mentions LangChain, LangGraph, create_agent, init_chat_model, @tool, ToolRuntime, agent middleware, human-in-the-loop, structured output, MCP tools (langchain.mcp, MCPAdapter), RAG, multi-agent handoffs or subagents, checkpointers, Deep Agents (create_deep_agent) or Managed Deep Agents (mda, define_deep_agent). Also trigger for migrating from create_react_agent, LLMChain, AgentExecutor or langchain-mcp-adapters, stream_events, SummarizationMiddleware, and langchain-classic.
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Practical security review and hardening guidance for application code in Python, JavaScript/TypeScript, and Go. Use this skill whenever the user asks for a security review or audit, wants to harden authentication (password hashing, sessions, JWT, OAuth2/OIDC, MFA), fix OWASP Top 10 issues (injection, XSS, IDOR, CSRF, SSRF, deserialization), threat-model with STRIDE, clean up secrets and .env handling, or set up dependency scanning (pip-audit, npm audit, osv-scanner, gitleaks, SBOM). Also trigger for "make this secure", "is this safe to deploy", bcrypt/argon2id, rate limiting logins, RBAC/ABAC, CSP and security headers, cookie flags, and secure-by-default coding help.
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Opinionated constraints for building better web interfaces with coding agents. Use this skill whenever the user asks you to build, review, or fix a UI — landing pages, dashboards, components, or any frontend code — and whenever you write Tailwind CSS, motion/react animations, accessible component primitives (Base UI, React Aria, Radix), or review agent-generated interface code for quality, accessibility, and performance. Also trigger for "make the UI better", "review my interface", "clean up this component", or any frontend work in React, Next.js, or plain HTML/CSS.
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Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.
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Systematically find and fix performance problems in applications, frontend and backend, by measuring first. Use this skill whenever the user reports slow endpoints, high latency, poor throughput, memory leaks, large bundles, or slow page loads; wants to profile with cProfile, py-spy, Chrome DevTools, Node --cpu-prof, or perf; run load tests with locust or k6; set performance budgets or SLOs; optimize database queries (EXPLAIN, indexes, N+1); add caching (Redis); improve frontend performance (code splitting, lazy loading, image optimization, memoization, virtualization); or tune the network (CDNs, gzip/br, HTTP/2/3). Also trigger for p50/p95/p99 latency, throughput, error rate, connection pooling, async vs threads, GIL, and memory leak investigations.
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Design and review intuitive, scalable, maintainable HTTP APIs. Use this skill whenever the user wants to design a new REST API, review an existing API or spec, write OpenAPI 3.x definitions, or work with HTTP semantics (GET/POST/PUT/PATCH/DELETE), status codes, idempotency (Idempotency-Key), error envelopes (RFC 7807 problem+json), pagination, filtering, versioning, or API auth (API keys, OAuth2 client credentials, rate limits). Also trigger for "API design", "RESTful", "endpoints", "OpenAPI", "Swagger", "ReDoc", "contract testing", or GraphQL and gRPC design questions.
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