Praxist Task Initialization
Convert an existing runnable computer-based research project into a formal Praxist task...
Install
npx skills add sapientinc/PRAXIST --skill praxist-task-initialization -g -a claude-code -y- Repository
- sapientinc/PRAXIST5.5k
- Category
- Data and ML
- Updated
- 1mo ago
- License
- See repository
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
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.
Praxist Task Initialization
Use this skill to turn a user's existing runnable research project into a Praxist task project. The output is a new task directory selected later with praxist start --task-path ...; do not edit Praxist core or generic plugins.
Mandatory Opening Banner
Before scanning or editing, display this prominently:
**IMPORTANT PRECONDITION**
Praxist task initialization assumes:
1. You already have all source code for the research project.
2. The project already runs smoothly on this machine in some environment/container.
3. Required datasets, simulators, benchmark fixtures, or control environments are already present or reachable from this machine.
If any item is false, stop and ask the user to provide the missing path, environment, dataset, or simulator before building the task.
When praxist-takeover, praxist-takeover-codex, or the user invokes this
skill to repair an existing task that
fails a required task-init check, preserve its established scientific
objective, evaluator protocol, metric directions, baseline, and runtime
ownership. Limit edits to the task harness components responsible for the
failed contract, rerun the same validations, and avoid rebuilding unrelated
task files. Ask the user before changing scientific selection semantics that
cannot be derived unambiguously from the existing task and project evidence.
Inputs
Resolve paths in this order:
- User-provided research project path.
- Current agent working directory.
Default output task path is <research_project>/praxist_task unless the user provides another output path. Do not mutate the original project except to read it and, if explicitly requested, run its documented smoke/evaluation commands.
Locate Task Templates
Resolve the template root before reading or copying a scaffold. Do not assume a source checkout exists after pip installation:
