Results for “decompilation”

Reverse engineers stripped Go and Rust binaries by recovering runtime metadata, symbols, panic strings, and idiomatic decompilation patterns.

Systematically tear down and remove a service, package, or infrastructure component

A stakeholder asks to make search faster.

Level 2+ task decomposition. Use when a single task from tasks.md is STILL too complex (affects 3+ files or requires multiple logic blocks). Breaks tasks into MICROTASKS (1 file/function each) for granular implementation. NOT for initial task breakdown - OpenSpec already does that.

Systematic debugging — reproduce, read the error, isolate, hypothesize, fix, verify, document. Use when tracking down a bug, unexpected behavior, or test failure — including phrases like "this doesn't work", "why is this failing", or "help me debug".

Step-by-step debugging workflow: reproduce the bug, isolate the failing component, trace to root cause, apply a targeted fix, and verify the fix resolves the issue without regressions. Use when you encounter a bug, error, exception, crash, or unexpected behavior that needs troubleshooting.

Breaks natural-language problem descriptions into sub-tasks suitable for DAG nodes. The entry point of the meta-DAG. Identifies phases, dependencies, parallelization opportunities, and vague/pluripotent nodes that can't yet be specified. Uses domain meta-skills when available. Activate on "decompose task", "break down problem", "plan workflow", "what are the steps", "sub-tasks", "task breakdown". NOT for executing the decomposed tasks (use dag-runtime), building the DAG structure (use dag-planner), or matching skills to nodes (use dag-skills-matcher).

Performs free, open-source reverse engineering with Ghidra using headless or GUI modes, including decompilation, cross-references, and optional MCP workflows when IDA is unavailable.

Deduplicating Training Data Makes Language Models Better

Use when investigating a bug, runtime error, unexpected behavior, test failure, or crash. For systematic root cause analysis, error message interpretation, stack trace reading, reproduction steps, and targeted fixes. Activate when the user says "it's broken", "this doesn't work", "I'm getting an error", or "help me debug".

Analyze .NET malware by decompiling and debugging assemblies with dnSpy, deobfuscating with de4dot, and extracting C2 configurations and IOCs.

Decompose work into small, verifiable tasks with explicit acceptance criteria, ordered by dependencies and sliced vertically for reliable implementation.

Coordinate end-to-end deployments across Stripe, Supabase, and Vercel using the Composio CLI. Update pricing, apply database migrations, deploy frontend, verify changes, and announce releases from a single script.

Diagnose and fix a bug through a disciplined reproduce, isolate, diagnose, fix, verify loop driven by evidence and one hypothesis at a time — not guesswork.

Guiding workflows by decomposing complex tasks (Epics) into Atomic Steps under 15 minutes each, with progress tracking and drift prevention. Use when complex decomposition is needed.

Autopsy a stuck project, extract salvageable artifacts, and route to a fresh start via $bootstrap-repo reset or new-repo bootstrap

4-phase systematic debugging methodology with root cause analysis and evidence-based verification. Use when debugging complex issues.

Fix broken or failing functionality through structured reproduction, root-cause analysis, minimal fix, and verification. Load when the user asks to fix a bug, debug an error, resolve an issue, or work on a Linear ticket. Also triggers on "this is broken", "fix this bug", "why is this failing", "debug this", "resolve this error", "what went wrong", or any request to diagnose and fix a problem.

Triage Linear or Jira backlogs and run bug sweeps via the Composio CLI. Bulk-fetch issues, dedupe, relabel, reassign, and post summaries — all from the shell without clicking through the UI.

Demystifying CLIP Data

Systematic root-cause analysis for bugs, errors, and unexpected behavior

Triage Linear or Jira backlogs and run bug sweeps via the Composio CLI. Bulk-fetch issues, dedupe, relabel, reassign, and post summaries — all from the shell without clicking through the UI.

Systematically repair a broken feature or module by scoping, tracing dependencies, diagnosing all issues, fixing them in order, and verifying end-to-end.

Run a reproduce → isolate → verify debugging workflow for concrete bugs, regressions, flaky failures, and environment-specific behavior. Use when the user already has a failing command, test, request, UI flow, or narrowed symptom and needs root-cause diagnosis or fix verification rather than raw log-line selection, broad test-policy design, PR review, or generic performance tuning.

Coordinate staged releases across Stripe, Supabase, and Vercel from the shell using the Composio CLI. One script kicks off the whole 'ship it' sequence: product/price updates, DB migrations, frontend deploy, smoke checks, changelog post.

Handles containerization, CI/CD pipelines, and deployment setup.

Build and debug Retrieval-Augmented Generation pipelines — chunking, embedding, retrieval, reranking

Edits real video footage through a structured pipeline: transcribe and plan with an AI agent, cut with FFmpeg, compose with Remotion, generate voiceover and music via ElevenLabs and fal.ai, then polish in Descript or CapCut.

Investigate a problem, log it to the debug changelog, cross-check past issues, and suggest a non-duplicate fix.

Diagnose bugs, failing tests, or unexpected behavior by reproducing, isolating, hypothesizing with evidence, and confirming fixes before making changes.

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

Systematically diagnose and fix software bugs by analyzing error messages, stack traces, logs, and runtime behavior across multiple languages. Use when the user requests debugging or provides relevant inputs for this workflow.

The intelligence layer of WinDAGs. Decomposes natural language tasks into Hierarchical Task DAGs (HTDAGs), matches subtasks to skills, executes waves in parallel, and dynamically expands nodes when complexity exceeds executor capability. Use for 'orchestrate', 'execute DAG', 'parallel agents', 'decompose task', 'coordinate skills'. NOT for single-skill tasks or simple linear workflows.