Results for “executor”

Execute programs on a compiled transformer stack machine where every instruction fetch and memory read is a parabolic attention head. Demonstrates that transformer attention + FF layers can implement a working computer. Use when user mentions "llm-as-computer", "lac", "stack machine", "compiled transformer", "percepta", "parabolic attention", "execute program", or asks to run/trace programs on the transformer executor.

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Expert guide for ephemeral WebAssembly (WASM) and isolated micro-runtime sandboxes to securely execute AI-generated code, validate database migrations in-memory, and perform sub-millisecond fuzzing / Panduan ahli sandbox WebAssembly (WASM) efemeral dan micro-runtime terisolasi untuk eksekusi kode AI yang aman, validasi migrasi database in-memory, dan fuzzing sub-milidetik.

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Clerk Backend REST API explorer and executor. Browse tags, inspect endpoint schemas, and execute authenticated requests. Use when listing users, managing organizations, or calling any Clerk API endpoint.

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CircleCI migration to GitHub Actions — syntax mappings and the migration report template for Workflows, jobs, Orbs, executors, contexts, parameters, matrix jobs, approval gates. Load with `migration-core` when migrating CircleCI sources to GitHub Actions.

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Use when a ChatGPT/Codex Plugin workflow needs deterministic computation, file processing, repository checks, package inspection, or other work that should be actually executed with host-native Python instead of answered from unverified reasoning.

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Pick among candidate outputs (code, configs, plans) by running them on diverse inputs and clustering by behavioural fingerprint, rather than by textual aggregation or log-probability. Activates when an executor returns multiple plausible candidates that need disambiguation, when output-majority voting would be the default choice, or when reviewing generated code that has not yet been validated. The 2026 evidence (Semantic Voting, arxiv 2605.08680v1) is that any execution-based selector dominates output-majority voting by 19-52pp; sketch-generated inputs beat random fuzz by 11.3pp. Triggers: "pick the best candidate", "majority vote on code", "select from N samples", "validate the generated output", "behavioural verification".

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Use when writing asyncio Python code — async/await coroutines, concurrent I/O with asyncio.gather, task creation and cancellation, semaphore rate limiting, producer-consumer queues, async context managers, async generators, WebSocket servers, aiohttp web scraping, async database operations, run_in_executor for blocking calls, or testing async code with pytest-asyncio. Covers FastAPI and aiohttp patterns, synchronization primitives, timeout handling, and common pitfalls like event loop blocking and missing await.

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Classifies raw pilot request → structured JSON {task_type, class, artifact, budget_estimate, confidence, routing_tag, resolution_path, schema_version, result_type, expected_result_kind, result_kind_resolution, hypothesis_relation}. Keyword-fast (<200ms) or Haiku fallback (<60s). Does NOT create WP or call executor.

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Executes Ansible playbooks for server diagnostics and remediation using ansible-runner Python SDK. Supports inventory parsing, vault-encrypted credentials, and real-time task output streaming.

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Use when a LibAFL fuzzer needs an executor, observer, feedback, mutator, scheduler, or objective composed around a target. Not for remote, credential, publish, deploy, or irreversible changes.

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Interactively gather parameters for `python -m cli.inference.text_generate`, generate a concrete text generation simulation command, explain assumptions, ask for execution confirmation, then run the command and summarize performance metrics. Use when the user wants to validate a specific model, hardware profile, batch/query size, prefill or decode scenario, TP/DP/EP/MOE parallel strategy, profiling database, chrome trace, empirical metrics export, or a best row produced by throughput_optimizer.

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Interactively gather parameters for `python -m cli.inference.throughput_optimizer`, generate a deployment simulation command, explain assumptions, ask for execution confirmation, then run the simulation and summarize the best parallel strategy. Use when the user wants to evaluate a model on one or more hardware profiles, compare hardware targets, compare aggregation versus disaggregation versus PD ratio optimization, choose TP/EP/MOE-DP search ranges, or obtain recommended concurrency, throughput, TTFT, and TPOT results from throughput modeling.

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The Arbor research-coordinator protocol: the OBSERVE -> IDEATE -> SELECT -> DISPATCH -> DECIDE cycle over a durable Idea Tree. Preloaded automatically on /auto-research sessions. The coordinator never edits code or runs commands — executors do that in isolated worktrees; the coordinator steers the tree with idea_tree / dispatch_experiments / merge_experiment.

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Scaffold a new Forge subagent (planner/architect/executor/reviewer/debugger/memory-style).

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Systematic debugging playbook for the Forge agentic loop (src/core/loop.ts and src/agents/executor.ts). Use when a task is stuck, looping, or emitting wrong tool calls.

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Systematic debugging playbook for the Forge agentic loop (src/core/loop.ts and src/agents/executor.ts). Use when a task is stuck, looping, or producing wrong tool calls.

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Scaffold a new Forge subagent (planner/architect/executor/reviewer/debugger/memory-style). Use when adding anything to src/agents/.

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Execute test suites with proper configuration, parallel execution, and detailed reporting. Use when running unit tests, integration tests, or full test suites.

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Delegate tasks to ANY CLI agent (claude, codex, aider, ...) running in a detached tmux session, with a race-safe done-signal protocol and multi-turn iteration. Use when delegating work to a non-Claude CLI agent, when the user says "tmux delegate", "run agent in tmux", "delegate to codex/aider", or when executor work should run in an observable background terminal instead of the Agent tool.

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Drafts a Chapter 11 Plan of Reorganization under 11 U.S.C. §§ 1121–1129 with claim classification, treatment provisions, distribution mechanics, executory contract determinations, and confirmation compliance. Use when preparing reorganization plans, restructuring debts, classifying claims, drafting Chapter 11 plans, or helping debtors emerge from bankruptcy. Trigger keywords: reorganization plan, Chapter 11, plan of reorganization, debt restructuring, cramdown, claim classification, confirmation order.

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Multi-file hypothesis testing with keep/discard loop, compound baselines, and experiment logging

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Policy-aware Safe portfolio rebalancing assistant for 31Third ExecutorModule.

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Wave-based parallel scheduling for DAG execution. Manages execution order, resource allocation, and parallelism constraints. Activate on 'schedule dag', 'execution waves', 'parallel scheduling', 'task queue', 'resource allocation'. NOT for building DAGs (use dag-graph-builder) or actual execution (use dag-parallel-executor).

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ALIAS for dag-orchestrator. The original DAG execution skill, now unified with orchestrator into dag-orchestrator. Use dag-orchestrator for the full HTDAG experience.

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