القائمة
Local sandboxed compute — Docker or host process. Use for quick / iterative work, MLX/Metal on Apple Silicon, anything that fits within local memory + disk. Free, no approval gate, supports stop.
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
Create or revise high-quality academic and research presentation slides in Markdown using Marp, following a story-first multi-phase workflow. Use when the user needs conference talk slides, lab or group meeting decks, thesis defense presentations, research seminars, invited talks, mixed-audience or industry research talks, OR wants to create, revise, or edit a talk deck or a specific slide of it (e.g. 'revise slide 7 of my conference talk'). Starts from an audience-facing story spine before slide titles, frameworks, screenshots, or feature lists; then applies Assertion-Evidence and Mayer multimedia principles. For teaching/lecture slides use `teaching-marp-slides`; for written documents use `paper-writing` or `scientific-writing`; for individual figures use `scientific-visualization`.
Create or revise lecture/teaching slides in Markdown using Marp for upper-undergraduate and graduate courses across any academic discipline. Use when the user needs classroom lecture slides, course materials, tutorial slides, or workshop sessions — including a request framed as creating or editing a slides Markdown file (e.g. a '*_slides.md' in a course or week folder) for teaching a class — or wants to revise existing teaching slides. Enforces Cognitive Load Theory, worked-example effect, scaffolding, and retrieval practice principles. Produces both a lecture version and a handout version. For research/conference talks use `academic-marp-slides`; for written documents use `paper-writing` or `scientific-writing`.
Strategically revise an existing CS/AI/Systems conference paper draft for resubmission or submission readiness. Focuses on framing diagnosis, claim crystallization, narrative unification, evidence strengthening, reviewer defense, and venue-aware polish.
Write publication-ready ML/AI/Systems conference papers (NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP). Use when the user requests a conference manuscript, needs conference-specific templates/checklists, or format conversion between venues. For journal articles or technical reports, use scientific-writing instead.
Core skill for high-quality research strategy, topic exploration, idea generation, scope/framing critique, contribution assessment, and mentor-style research direction refinement. Use when the user asks whether an idea is worth pursuing, wants brainstormed research directions, needs to pivot, sharpen a paper/proposal scope, evaluate novelty, identify the strongest objection, or turn a vague area into a concrete, testable research plan. Produces a small number of high-bar candidates with premise audits, nearest alternatives, decisive experiments, and kill criteria.
Prompt guidance for the generate_diagram tool — how to describe different scientific diagram types (flowcharts, pathways, architecture, circuits, networks, conceptual) so the tool produces publication-grade output on the first iteration.
| name | compute-local |
| description | Local sandboxed compute — Docker or host process. Use for quick / iterative work, MLX/Metal on Apple Silicon, anything that fits within local memory + disk. Free, no approval gate, supports stop. |
| category | Compute |
| tags | ["compute","local","sandbox"] |
| triggers | ["run script","python","train locally","fine-tune locally","data processing"] |
| license | MIT |
| metadata | {"skill-author":"Research Copilot"} |
Local backend (backend: 'local', toolPrefix: 'local') runs scripts in a sandbox on the user's machine — Docker when available, host process otherwise. No cost, no approval gate, supports stop.
sandbox: 'process').list_compute_backends() to confirm local is registered + available.compute_plan({ backend: 'local', command, task_description, script_path? }) — produces a plan with risk assessment, recommendations, and an experience summary if you've run similar tasks before.local_execute({ plan_id, timeout_minutes? }) — kicks off the run.local_status({ run_id }) or block with local_wait({ run_id, timeout_seconds? }).local_stop({ run_id }).When the planner detects --smoke / --dry-run / --validate flags in the script's argparse/click config, it sets smokeSupported: true and recommends a quick validation run before the full execution. The runner will automatically run the smoke command first; if it fails, the full run is not attempted.
The planner recommends one of:
docker — preferred on Linux with Docker installed; isolates filesystem + network.process — used on Mac (no GPU passthrough in Docker) or when Docker isn't installed.You can override via the sandbox field in backend_data.recommendations when building the plan, but the recommendation is usually correct.
When a run fails, local_status returns a failure field with a code (e.g. OOM_KILLED, MODULE_NOT_FOUND, TIMEOUT) and human-readable message + suggestions. Retry by calling compute_plan + local_execute again with parent_run_id set to the failed run id — this preserves the retry lineage for the experience tracker.
Print structured progress on stdout:
##PROGRESS## {"step": 1, "total": 5, "phase": "loading", "percentage": 20}
The runner extracts these and surfaces them through local_status/local_wait.