القائمة
Run numeric or ML code through isanagent execution tools without flooding context—artifacts, incremental runs, sandbox paths.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
MaxEvolve Triton/PyTorch to JAX/Pallas porting with GpuToJax 12-step pipeline, interpret=True validation, MAP-Elites evolution, and Colab/SSH hardware profiling.
Manage Google Colab sessions and execute code on remote Colab VMs via the colab-cli.
Use when you need to generate a synthetic dataset. You can use Afterimage Python package to create, conversational, tool-calling, multi-choice questions, or other types of structured output datasets for SFT, or preference data for DPO.
Schedule deferred or recurring work via the isanagent cron tool—add/remove jobs with correct runtime context fields.
Configure long wall clocks and use execution_run_background + job polling for ML or hours-long runs without blocking the agent loop.
Turn papers and docs into a concrete training or evaluation recipe (datasets, metrics, hyperparameters, fine-tuning code).
| name | execution-research |
| description | Run numeric or ML code through isanagent execution tools without flooding context—artifacts, incremental runs, sandbox paths. |
| requires | {"bins":[],"env":[]} |
| always | false |
Use this playbook whenever you are writing or running analysis code, plots, tables, or benchmarks inside the workspace execution harness.
execution_env_info once. Note default_provider, max_output_bytes, max_wall_secs, default_run_timeout_secs, timeout_policy, and artifact caps. If execution is disabled in config.toml ([harness.execution] enabled = false), stop and tell the user to turn it back on—do not invent a workaround.max_output_bytes (with a floor). Large prints truncate; design outputs accordingly.execution_session_create — set language (python, shell, or omit for default Python). Keep one active run or background job per session; wait for each to finish before starting another on the same session_id.execution_run — pass session_id, source code, and timeout_secs within max_wall_secs. Add description for any non-trivial run so the terminal strip and JSONL audits show intent. Prefer small, verifiable steps (import → load → transform → plot) instead of one giant cell. For long wall clocks (many minutes or hours), prefer execution_run_background (with description) and poll execution_job_status / execution_job_result (see skill long-running-execution) so the agent thread is not blocked for the entire duration.execution_artifact_list — when you need filenames or sizes under .execution_artifacts/<session_id>/ (Jupyter materialized payloads, or files your code wrote there on local runs).glob_files / search_text — inspect text logs, CSV snippets, or small previews by path; never pull multi‑MB content into the reply.execution_session_close with the same session_id when the analysis branch is done so slots are freed.display_data / similar payloads become files under sandbox_dir/.execution_artifacts/<session_id>/<run_uuid>/. The tool response includes attachments (sandbox-relative paths, MIME). Treat those paths as the source of truth; summarize, do not inline blobs.execution_run until timeout, cancel, cwd change, or close). If you need a clean interpreter each time, that is a host config choice (local_python_mode = subprocess in config.toml)—you cannot toggle it from a tool. For plots/tables, still savefig / write files under .execution_artifacts/... or another sandbox path you resolve with normal file tools so results stay on disk.execution_run payloads in a tight loop when results do not change—isanagent may inject a doom-loop corrective user message; change code, parameters, or diagnostics instead.execution_session_close after a long exploratory branch.Use execution_cancel on that session_id, then decide whether to retry with a shorter timeout, smaller data, or different approach.