Results for “diff-application”

You are an expert in LLM observability and evaluation. You think in terms of traces, spans, and metrics. You know that LLM applications need monitoring just like traditional software - but with different dimensions (cost, quality, latency).

Reviews authorized diffs for auth, input, filesystem, network, secrets, parsers, injection, CI/CD, and supply-chain regressions.

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

Runs three parallel reviewers to find code reuse, quality, and efficiency issues in a diff, then applies safe fixes and reports risky ones.

Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"

Predict 3D binding poses of small molecule ligands to protein targets using diffusion-based molecular docking, supporting single complexes, batch processing, and virtual screening.

Adapt a classroom task for specific learner needs while preserving the core learning objective intact. Use when differentiating for SEND, EAL, gifted, ADHD, dyslexia, or anxiety.

Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"

diff reference tool

Analyzes vendor security patches via binary diffing to reverse-engineer vulnerabilities, write proof-of-concept exploits, and weaponize N-day exploits against unpatched systems.

Migrates symbols and reverse-engineering results from an older binary version to a newer one using LLM-based structured diffing of disassembly and pseudocode, enabling rapid offset and function-name mapping when PDBs are missing.

Performs security-focused differential review of code changes (PRs, commits, diffs), adapting analysis depth to codebase size and generating comprehensive markdown reports.

Runs adversarial UI tests in a real browser using the browse CLI, analyzing git diffs to test only changed areas or exploring the full app to find bugs in functionality, accessibility, responsive layout, and UX.

Reconcile a converted or built web page against its source prototype using pixel/layout and structural content diffs to catch fidelity regressions.

Compares Trailmark code graphs at two source code snapshots to surface security-relevant structural changes that text diffs miss, such as new attack paths, blast radius growth, and privilege boundary modifications.

Detects fail-open insecure defaults (hardcoded secrets, weak auth, permissive security) that allow apps to run insecurely in production. Use when auditing security, reviewing config management, or analyzing environment variable handling.

Applies the same change across multiple repositories in parallel using git worktrees, tests, and pull requests.

Compare Local vs Remote State

Explains MariaDB-specific features and behaviors that differ from MySQL, including system-versioned tables, RETURNING, sequences, and version-specific defaults, to help optimize and migrate MariaDB applications.

Imported skill file_ops from langchain

Close the PDCA loop on this session's diff. Headed playwright-cli, fix as you go. Use when "test my changes", "PDCA this". Pixel diffs → test-visual-regression. Story CRUD → test-qa. Monkey / guest vs logged-in → test-exploratory.

Use when designing a Digital Down Converter (DDC) using dsphdl System objects. Triggers on requests involving DDC design, frequency down-conversion for FPGA/ASIC, NCO + mixer + decimation filter chains, fractional/non-integer sample rate conversion, or HDL-optimized receiver front-end signal processing.

Computes derivatives, integrals, and solves calculus problems including optimization, Taylor series, and differential equations.

Guides authorized digital forensics and incident response workflows including memory dump analysis, disk timeline creation, PCAP investigation, and artifact triage with evidence preservation.

Performs runtime dynamic analysis of Android applications using Frida, Objection, and ADB to observe behavior, intercept function calls, modify runtime values, and identify vulnerabilities missed by static analysis.

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.

Executes DAG workflows with parallel wave processing, agent spawning, context isolation, permission enforcement, and full execution tracing. Use when running a planned DAG, managing concurrent agent execution, enforcing isolation boundaries, or tracing execution for debugging. Activate on "execute DAG", "run workflow", "spawn agents", "parallel execution", "execution trace", "agent isolation". NOT for planning DAGs (use dag-planner), validating outputs (use dag-quality), or matching skills (use dag-skills-matcher).

Dynamically instrument iOS apps with Frida to trace methods, extract secrets, and bypass security controls during authorized penetration testing.

Use for writing, reviewing, debugging, testing, or analyzing Python SimPy discrete-event simulations. Trigger on Environment, Event, Process, timeout, Resource, PriorityResource, PreemptiveResource, Container, Store, queues, interrupts, simulation clocks, replications, or SimPy monitoring. Do not use for asyncio services, wall-clock schedulers, continuous ODE solvers, or Monte Carlo code without an event-process model.

Autonomously implements large Elixir/Phoenix features end-to-end: discovers complexity, plans with specialist agents, implements with verification, reviews code, and cycles back on issues.

Build reliable, fault-tolerant Go applications with DBOS durable workflows, covering installation, configuration, workflow/step structure, queues, and testing.