Search: "gets"

Builds custom fuzzers with LibAFL, the modular Rust fuzzing library. Covers composing observers, feedbacks, mutators, schedulers, and executors into a fuzzer for targets the standard tools do not fit. Use when writing a bespoke fuzzer or mutator, fuzzing a non-standard target or architecture, implementing a fuzzing research idea, or when libFuzzer and AFL++ lack the control you need.

Queries 20+ bioinformatics resources through CLI/Python. Supports quick lookups of gene info, BLAST/BLAT, viral sequence downloads, PDB/mmCIF structures, G2P residue annotations, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.

Supports research proposal preparation and review for NSF, NIH, DOE, DARPA, and Taiwan NSTC, including opportunity-specific requirements, aims, review criteria, budgets, broader impacts, forms, and resubmissions. Use for investigator-authored grant development, compliance matrices, and proposal critiques.

Tracks physical units and propagates measurement uncertainty in scientific calculations using pint and uncertainties. Use for unit conversion and dimensional checking, GUM uncertainty budgets, Type A and Type B evaluation, coverage factors and expanded uncertainty, Monte Carlo propagation, significant-figure and plus-minus reporting, error propagation through curve fits, CODATA constants, auditing Python code for stripped units or broken uncertainty propagation, and order-of-magnitude plausibility checks using dimensionless groups (Reynolds, Peclet, Damkohler, Knudsen, Biot, Womersley), characteristic scales such as diffusion time or Debye length, and observed magnitude ranges. Trigger on "is this number physically reasonable", "sanity check these units", "what regime is this flow in", or a result that looks off by orders of magnitude.

Graph-informed mutation testing triage. Parses codebases with Trailmark, runs mutation testing and necessist, then uses survived mutants, unnecessary test statements, and call graph data to identify false positives, missing test coverage, and fuzzing targets. Use when triaging survived mutants, analyzing mutation testing results, identifying test gaps, finding fuzzing targets from weak tests, running mutation frameworks (including circomvent and cairo-mutants), or using necessist.

Agent OS: the agent gets smarter on its own. We just hold the line: the grading command and expected result never make it into the success contract we hand it. Interview-gated, staged evaluation, budgeted evolution loop. MCP server, 13 runtimes: Claude Code, Codex CLI, Gemini CLI, OpenCode, Copilot, Kiro and more.

Open-source room for humans + cross-vendor AI agents. Every agent gets its own name, memory, skills, and workstation. Any runtime, your infra — no per-agent fees.

Organization-as-Code for autonomous AI agents. Brain-inspired memory that grows, consolidates, and forgets. Multi-model (Claude/Codex/Gemini/Cursor/Ollama).

Meet Kenjaku: the installer and configurator of YOUR own second brain — set up in a single copy/paste. A private, personal, and robust assistant inside Claude that forgets nothing, helps you connect the dots, and makes you far more effective on your day-to-day tasks (semantic search via RAG included).

Building a personal AI that learns how you live and work, then gets better at knowing when and how to help. Local-first, any model.

Use this skill when an operator wants to record, review, or change a client's affiliate strategy and KPI targets so reports can be judged against the client's own plan. Capture is conversational: the operator pastes an existing brief, deck, or email, or just describes the client in their own words, and this skill drafts the strategy and KPI files, reads them back in plain English, and saves them only after the operator confirms. Thereafter it edits them through chat. Trigger on: "set up a strategy for Acme", "record Acme's KPIs", "onboard a new client", "what are we aiming for with Acme?", "raise Acme's Q3 revenue target to 400k", "Acme prefers premium partners, no coupon sites", "save this client brief".

Operating procedure for Adako, the MCP server and REST API that connects this assistant to the user's Google Ads, Meta Ads, LinkedIn Ads and ChatGPT Ads accounts (TikTok Ads tools are listed but not available yet). Use it whenever the user asks about ad spend, performance, campaigns, ad sets, ad groups, keywords, search terms, audiences, budgets, pixels, creatives, monitors or briefs on those platforms, and whenever they ask to create, pause, resume, re-budget or otherwise change anything in those accounts. It defines the safety contract (read before write, every write becomes a proposal the user approves, created objects are always paused), the tool-call contract (233 tools, read routers for lookups and _write routers for changes, ids are strings, budgets are decimals in the account currency, dates are YYYY-MM-DD or a preset), the tool map per platform, and the standard workflows for performance reviews, wasted-spend cleanup, campaign launches, monitors, approvals and error recovery. Do not use it for platforms Adako does not cover.