primeintellect-ai
- 24 skills
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- 4 hours ago last updated
- ▌ Training · primeintellect-ai bundleLaunch and monitor prime-rl training runs. Use when starting, supervising, or debugging an RL/SFT run. Routes to `start-run` (entrypoints + how to launch) and `monitor-run` (logs, metrics, check-ins).
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- ▌ Brainstorm · primeintellect-aiRun interactive brainstorming across verifiers tasksets, evaluations, GEPA, and RL training. Use when the user wants ideation, literature scanning, concept teaching, roadmap planning, or research program design grounded in local CLI sources, verifiers, and RL trainer code.
- ▌ Create Environments · primeintellect-aiCreate or migrate native verifiers.v1 taskset, environment, and harness packages. Use to build a taskset, port a benchmark, add task tools, script or model a user, build a multi-agent environment, package an agent harness, or migrate an existing v0 environment to the typed v1 trace model.
- ▌ Evaluate Environments · primeintellect-aiRun and evaluate verifiers tasksets. Set up the necessary config files and observe the runs and their results.
- ▌ Configs · primeintellect-aiHow the prime-rl config system works — TOML files, CLI overrides, composition, and special patterns. Use when creating configs, debugging config errors, or overriding values via CLI.
- ▌ Install · primeintellect-aiHow to install prime-rl and its optional dependencies. Use when setting up the project, installing extras like DeepEP for multi-node expert parallelism, or troubleshooting dependency issues.
- ▌ Kernels · primeintellect-aiHow prime-rl vendors, builds, and ships CUDA kernels (the `deps/prime-kernels` submodule and the `prime-kernels` wheel). Use when adding a kernel, building it locally, calling one from training code, or publishing prebuilt wheels.
- ▌ Dashboard · primeintellect-aiFind, start, use, and stop the local run dashboard for metrics, configs, traces, logs, and reports. Use when asked for its URL, to watch or inspect a run, to control the open dashboard, or to create a cited dashboard report explicitly requested by the user.
- ▌ Start Run · primeintellect-aiHow to launch prime-rl training runs — the `rl`, `sft`, `inference`, and `evals` entrypoints, their config classes, and single-node/SLURM/dry-run modes. Use when starting a run or picking the right entrypoint.
- ▌ Monitor Run · primeintellect-aiMonitor an ongoing prime-rl training run — find the output directory, tail logs, check key metrics, inspect SLURM jobs, and restart safely. Use when asked to check on a run, debug training, or investigate performance.
- ▌ MCP · primeintellect-aiUse external MCP services generically from Python - search the supported-service catalog, inspect the user's connections, discover live tool schemas, and call tools on any connection (Notion, Linear, Slack, and the rest of the catalog) without per-service packages.
- ▌ Edit · primeintellect-ai bundleReplace an exact, unique string in an existing file. Use for targeted single-occurrence edits to files from the Python kernel instead of rewriting the whole file.
- ▌ Goal · primeintellect-ai bundleManage the persistent thread goal from the Python REPL. Use to read goal status and budget usage, to start a goal when the user explicitly asks for one, or to mark the active goal complete once its objective is fully achieved.
- ▌ Refine · primeintellect-ai bundleTrigger continual harness refinement from the Python REPL. Use when you notice a repeated failure, reusable tactic, delegation role, or behavior policy that should be persisted as a harness entry. Returns immediately; refinement runs when the current turn ends.
- ▌ Compact · primeintellect-ai bundleCheck context usage and compact the conversation from the Python REPL. Use when context is filling up and substantial work remains, so the session is summarized and you keep working instead of stopping early.
- ▌ Websearch · primeintellect-ai bundleSearch Google via the Serper API. Configure access via /login, then MCP Connections, then Serper (web search). Takes one query and returns titles, URLs, snippets, and knowledge-graph data.
- ▌ Attach Image · primeintellect-ai bundleLoad an on-disk image (PNG, JPEG, GIF, WebP) into the model's context as a viewable attachment so the model can directly SEE it — for screenshots, diagrams, charts, photos, or scanned pages. Use this when you need to perceive an image's visual contents. Requires a vision-capable model; errors clearly otherwise.
- ▌ Agent Message · primeintellect-ai bundleMessage an agent's parent, siblings, or direct children through the daemon. Discover reachable agents with agent_observe.list_agents, then send direct text without spoofing sender identity.
- ▌ Agent Observe · primeintellect-ai bundleRead-only roster and observation of an agent's parent, siblings, and direct children. Use to discover reachable agents and to inspect family status and bounded recent-message previews without mutating sessions.
- ▌ Rlm Heartbeat · primeintellect-ai bundleManage agent-owned RLM heartbeats from the Python REPL. Use when the user asks the agent to start, create, schedule, or manage a heartbeat, unless they explicitly request the user's /heartbeat.
- ▌ Skill Creator · primeintellect-ai bundleCreate, validate, and install Prime Agent skills - both markdown skills and Python-backed skills callable from the Python kernel. Use when the user asks to create a skill, turn a workflow, script, or prompt into a reusable skill, add a Python skill the agent can call, or asks how to write a SKILL.md and where skills live.
- ▌ Prime Intellect · primeintellect-ai bundleWork with Prime Intellect products via the prime CLI and Python SDKs - verifiers environments and the Environments Hub, evaluations (local and hosted), Hosted Training and prime-rl, code sandboxes, Prime Inference, GPU compute (pods and clusters), storage, and tunnels. Use when a task involves Prime Intellect, the prime CLI, verifiers, RL environments, evals, training, sandboxes, renting GPUs, Prime Inference models, or when the user asks what Prime Intellect is or what it offers.
- ▌ Evaluation · primeintellect-aiInstall and run a verifiers environment — smoke testing during development and full benchmark evals. Covers the `eval` CLI for the tasksets. Use while developing/iterating on an environment, when running/evaluating/benchmarking one, or when the user mentions eval, tasksets, eval runs, or smoke tests.