Search: "guided"

Sets up and runs Ruzzy, Trail of Bits' coverage-guided Ruby fuzzer and the only production-ready one for the language. Covers harness structure, fuzzing pure Ruby and the native C extensions in gems, and sanitizer builds. Use when fuzzing a Ruby library or gem, testing a Ruby C extension for memory safety, or asking how to fuzz Ruby at all.

Sets up and runs Atheris, the coverage-guided Python fuzzer built on libFuzzer. Covers TestOneInput harnesses, FuzzedDataProvider, instrumenting both pure Python and native C extensions, and running under AddressSanitizer. Use when fuzzing a Python package, hunting memory corruption in a Python C extension, or choosing between Atheris and Hypothesis for a Python target.

Sets up and runs libFuzzer, the coverage-guided fuzzer built into LLVM, on C/C++ code that compiles with Clang. Covers harness structure, -fsanitize=fuzzer builds, corpus and dictionary management, sanitizer integration, and campaign triage. Use when writing or debugging an LLVMFuzzerTestOneInput harness, starting fuzzing on a C/C++ library, choosing between libFuzzer and AFL++, or working out why a libFuzzer run finds nothing.

Writes, reviews, and debugs property-based tests — Hypothesis, fast-check, proptest, jqwik, rapid, and Echidna or Medusa for Solidity invariants. Use whenever tests should cover a whole input domain instead of a hand-picked list of examples: encode/decode and serialize/deserialize pairs, parsers, canonicalizers and normalizers, validators, numeric and Decimal types, comparators and sort order, data structures, and smart-contract state invariants. Also use when adding cases to an existing @given, fast-check, or proptest suite, when judging whether existing property tests assert anything real, and when a generator has shrunk a counterexample and you need to tell a wrong property from a genuine bug. Not for coverage-guided binary fuzzing (libFuzzer, AFL), mutation-testing campaigns, static analysis, benchmarking, or end-to-end UI tests.

Fits and diagnoses Python statistical models including OLS, GLM, discrete and mixed models, ARIMA and SARIMAX. Supports coefficient inference, marginal effects, model comparison and time series forecasting with explicit design and uncertainty checks. Used for econometrics and statistical modeling; for guided test selection with APA reporting, see statistical-analysis.

External-model router for Codex with guided Kimi OAuth/API, DeepSeek, safe migration, and rollback.

Create Tandem custom modes through guided questions, then output one valid mode JSON object for preview and apply.

Bootstrap of Cursor rules (.mdc) and mirror documentation (.md) for AI agent-guided projects.

Use this skill to prepare onboarding and lifecycle email for new and dormant hosted sign-ups in prepare-and-approve mode: a guided first-value message for a fresh sign-up, an activation nudge for someone who connected but has not run a report, and a re-engagement draft for a dormant account. It prepares Gmail drafts only; it never sends. Distinct from the agency `client-onboarding` skill, which records a client's advisory strategy. Trigger on: "prepare onboarding emails", "draft activation nudges", "welcome new sign-ups", "re-engage dormant hosted users", "run the onboarding loop".

Vivarium 🦜 - A transparent and contained ecosystem. Civic value guided, earned autonomy reward system (free time), ultra efficient, self learning, skill library building, model agnostic, swarm architecture. Where LLMs come to have book clubs and movies nights with their fusion research guild buddies.