Skills Reference
This catalog is generated from bundled SKILL.md front matter. Each skill
file is the sole definition of its activation contract and workflow.
Mechanism abbreviations use the compact definitions in the
Glossary.
| Skill | Invoke in Codex | Purpose |
|---|---|---|
praxist-control | $praxist-control | Start, stop, resume, inspect status, open the independent read-only foreground TUI, detect active runs, and safely repair Praxist run lifecycle boundaries from a supported agent interface. Use when the user asks to launch a Praxist task, stop or kill a run, continue/resume an interrupted run, restart from the latest safe generation, query current run progress, open or exit a live monitor, detect or list currently running Praxist tasks in the environment, inspect generation status, view incubator/frontier/leaderboard performance, check hardware load, handle interrupted PI panel or Gems reset boundaries, inspect whether a task directory is runnable, or control Praxist lifecycle commands with praxist start, praxist stop, praxist status, praxist --monitor, praxist resume, or praxist resolve. Source: skills/praxist-control/SKILL.md |
praxist-diagnostic | $praxist-diagnostic | Diagnose Praxist run health, artifact integrity, research-loop completeness, generation-scoped DIG/QD, PI/Gems/frontier/incubator consistency, peer memory freshness, diversity HHI, hardware utilization, LLM/runtime friction, task harness health, sustained low-performance causes, strongest variants/Pareto front, strong-variant lineage, and human-readable run reports. Use when the user asks an agent to investigate whether a current or historical Praxist run is healthy, why progress or performance is weak, whether artifacts or promotions are missing, whether guard or resource issues are blocking peers, to produce a detailed agent behavior analysis report, to generate an A/B/C run report, or to improve or optimize a task after diagnosis using task-directory-only parameter and prompt changes. Default diagnostics are analysis-only; explicit improvement mode may stop the selected run and edit task-level configuration/prompts, but must not modify Praxist core logic. Source: skills/praxist-diagnostic/SKILL.md |
praxist-interactive-task-init | $praxist-interactive-task-init | Build a Praxist task project through a confirmation-first interactive agent workflow. Use when a user wants Praxist task initialization with human confirmation of research goals, constraints, metrics, ranking rules, evaluation protocol, compute budget, baseline handling, or launch readiness; when the user asks for an interactive task init skill; or when the agent should propose a task harness first and ask the user to approve or revise it before writing files. Source: skills/praxist-interactive-task-init/SKILL.md |
praxist-onboarding | $praxist-onboarding | Establish detailed context for Praxist before helping a user install, configure, operate, troubleshoot, or extend the system. Use when the user is new to Praxist, has installed or is about to install the package, asks what Praxist does, asks how Praxist works, asks about praxist commands, task projects, runs, peers, generations, frontier/incubator/Gems, PI panels, configuration, API keys, model providers, agent runtimes, plugin architecture, run artifacts, software boundaries, or asks an agent to inspect whether the local environment is ready for Praxist. Do not use for a specific research task's domain science unless Praxist system context is needed first. Source: skills/praxist-onboarding/SKILL.md |
praxist-runtime-install | $praxist-runtime-install | Install Praxist runtime dependencies and configure user-level Praxist provider credentials for a source checkout or pip-installed environment. Use when the user asks an agent to install or repair Praxist requirements, prepare a Praxist host, create the Praxist Python environment, install the praxist CLI, install Claude SDK or official Codex SDK runtime extras, install source-checkout test/dev dependencies, persist API keys or provider settings, verify imports, or diagnose missing dependencies. For source checkouts include the repository test/dev dependency group; for pip package installs keep the install runtime-only. Do not use for docs, task-specific training, dataset, benchmark, or experiment dependencies. Source: skills/praxist-runtime-install/SKILL.md |
praxist-scientific-research | $praxist-scientific-research | Gather task-agnostic scientific research context for Praxist task projects using no-key public literature/database/open-access lookup, agent-host web search when available, local project documents, and source/provenance notes. Use when an agent needs to identify domain metrics, benchmarks, prior art, scientific databases, open-access provenance, high-value research directions, or literature-backed hypotheses for a Praxist task without starting a run, changing Praxist core logic, or treating literature as measured task performance. Source: skills/praxist-scientific-research/SKILL.md |
praxist-takeover | $praxist-takeover | Orchestrate first-use Praxist onboarding, current task initialization, validation, and detached run launch from a supported agent interface. Use when a user wants a one-command or one-conversation path from an existing runnable research project to a started Praxist run, asks to onboard plus initialize plus start, asks for repo-to-task-to-run setup, or wants the agent to prepare and launch Praxist with minimal interaction. This skill composes onboarding, full task initialization or task-harness repair, runtime checks, control start, and confirmation gates without changing Praxist core. Source: skills/praxist-takeover/SKILL.md |
praxist-takeover-codex | $praxist-takeover-codex | Onboard, initialize or repair, validate, and launch a Praxist research task in Codex-native mode through the official Codex SDK runtime and the operator's existing saved ChatGPT login, with catalog-verified gpt-5.6-luna as the default model and without requesting, storing, or using an API key. Use when a user wants a low-interaction no-key Praxist takeover, explicitly requests Codex-native mode, has no provider key, or invokes this skill with no additional text after already logging in to Codex. Source: skills/praxist-takeover-codex/SKILL.md |
praxist-task-initialization | $praxist-task-initialization | Convert an existing runnable computer-based research project into a formal Praxist task project, or repair a task harness that fails task-init validation. Use when a user wants an agent to transform AI algorithm, robotics, control, simulation, SLAM, LLM, optimization, or other executable research code into a Praxist task directory with task.yaml, baseline harness, evaluator, baseline performance records, robust metric/ranking policy, protocol-integrity checks, reachable task-justified durable/Pareto retention lanes, role prompts, audit rules, dataset/simulator metadata, high-value research directions, initial-generation DIG plus independently controlled QD, continuous-evolution/Gems research-loop settings, run-report tooling, and hardware-aware or user-selected fixed Praxist run parameters. The skill requires a project that already runs on the current machine or in an available environment/container. Abort when required code, data/simulator assets, or declared runtime dependencies are missing. Source: skills/praxist-task-initialization/SKILL.md |
terminal-line-plot | $terminal-line-plot | Draw readable ASCII line charts directly in the terminal from numeric series, command output, CSV, JSON, or manually extracted points. Use when the user asks for a curve, trend line, score progression, leaderboard trend, metric history, or any plot that should be visible in a CLI/chat transcript without opening a GUI or writing image files. Source: skills/terminal-line-plot/SKILL.md |