2.3K skills · sorted by relevance
Move Python execution to saved script files when execute_code_sandbox repeatedly fails or becomes unstable. The skill covers writing single or multiple files, running them through run_shell, inspecting outputs and errors, and removing temporary scripts afterward.
Continue Python tasks when sandbox execution fails by saving code as a script and running it through run_shell. The workflow covers dependencies, execution checks, and output verification for PDFs, image processing, data analysis, and file operations.
Recover from failed inline Python execution by saving the code as a script, running it through the shell, and checking the output. The four-step workflow helps diagnose errors in multiline code and spreadsheet generation tasks.
Continue library-dependent Python tasks when sandbox execution fails by switching to a shell script. Covers recognizing import errors, preserving script text, checking execution output, and troubleshooting package availability for tasks such as PDF generation or visualization.
Generate PDF documents when sandbox execution fails with unclear errors. Check available Python libraries, switch to direct shell execution, and verify the resulting file while collecting error details to help diagnose problems.
Debug repeated execution failures by saving Python code to a script file and running it through run_shell. Keep the script available for inspection, review intermediate output and errors, and edit it for another run.
Recover from repeated sandbox execution failures by saving Python code to a script and running it through run_shell. Check output, error messages, and exit codes to confirm results or diagnose remaining execution problems.
Run Python code through a saved script and shell when repeated sandbox failures point to environment problems. Captures execution output and checks exit codes and expected files to verify whether the task completed.
Build isolated code execution environments on Cloudflare Workers for AI-generated code, scripts, and build pipelines. Covers running commands, managing files, preserving interpreter state, exposing service previews, and cleaning up sandbox resources.
Builds resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with saga pattern, testing with LocalDurableTestRunner. Triggers on phrases like lambda durable functions, durable execution, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, reliable serverless applications, context.step, context.wait, context.invoke, context.runInChildContext, withDurableExecution, DurableContext, UnrecoverableInvocationError, durable-execution-sdk, qualified ARN invocation, and durable handler replay.
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Performs symbolic execution to detect potential errors by exploring execution paths, solving path constraints, and generating test inputs. Use when you need to analyze code for bugs like null dereferences, division by zero, buffer overflows, or assertion violations. Also use to generate test inputs that exercise different code paths, find edge cases, or explore all reachable program states. Supports Python, Java, and C/C++ through manual symbolic execution techniques and integration with tools like KLEE, angr, Z3, and Symbolic PathFinder.
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Instruments programs to capture execution traces specifically for reproducing reported bugs, enabling consistent replay and diagnosis of failures. Use this skill when you need to reproduce a bug, capture execution traces for debugging, instrument code to record program behavior, generate replay scripts for bug reproduction, diagnose hard-to-reproduce failures, or perform deterministic replay of program execution. Triggers when users ask to instrument code for tracing, capture execution traces, reproduce bugs, generate replay scripts, or enable deterministic debugging.
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Automatically instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation levels.
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Structures best execution monitoring with venue analysis, systematic internalization assessment, and regulatory compliance documentation. Use when managing best execution, documenting execution decisions, or conducting venue analysis.
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Uses symbolic execution (angr) to solve constraint-based malware problems — finding inputs that reach a target branch, recovering keys/passwords from a check routine, and brute-forcing opaque predicates — by generating a ready-to-edit angr harness. Activates for requests to use symbolic execution, solve a malware unlock check with angr, or recover an input that reaches a code path.
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Consumer Goods Cloud setup: visit execution, retail execution, route planning, in-store task management, compliance checks, off-shelf detection, image recognition. NOT for territory planning and route optimization for sales reps - use integration/salesforce-maps-setup. NOT for B2B Commerce storefronts - use admin/b2b-commerce-store-setup.
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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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Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).
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Time-travel debugging for DAG executions. Inspect agent state at any node, replay decisions with modified inputs, compare execution traces side-by-side, and identify where reasoning diverged. Inspired by LangGraph Studio's state-editing model and Temporal's event history. Activate on "debug DAG", "replay execution", "time travel debug", "inspect node state", "what went wrong at step", "compare runs", "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.
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Builds, validates, schedules, and dynamically modifies DAG execution graphs. Decomposes problems into nodes with dependencies, performs topological sorting, detects cycles, resolves conflicts, and schedules wave-based parallel execution. Use when designing a DAG structure, validating dependencies, planning execution order, or modifying a DAG at runtime. Activate on "build DAG", "plan workflow", "DAG dependencies", "topological sort", "schedule execution", "modify DAG", "replan". NOT for executing DAGs (use dag-runtime), validating outputs (use dag-quality), or matching skills to nodes (use dag-skills-matcher).
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Profiles DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities. Activate on 'performance profile', 'execution metrics', 'latency analysis', 'token usage', 'cost analysis'. NOT for execution tracing (use dag-execution-tracer) or failure analysis (use dag-failure-analyzer).
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Profiles DAG execution performance including latency, token usage, cost, and resource consumption. Identifies bottlenecks and optimization opportunities. Activate on 'performance profile', 'execution metrics', 'latency analysis', 'token usage', 'cost analysis'. NOT for execution tracing (use dag-execution-tracer) or failure analysis (use dag-failure-analyzer).
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Review Perl code that handles user input, files, external commands, databases, and web output. Use concrete examples and a security checklist to identify unsafe patterns and apply input validation, parameterized queries, and appropriate output encoding.
Review prediction-market and trading-agent workflows before they handle account access, portfolio data, or trade planning. Produces structured findings on venue rules, data quality, security, privacy, and execution risks, with required safeguards and a safe next step.