ai-dynamo

Runs deterministic byte-parity and paired performance campaigns for Dynamo offline KV-aware replay across native-G1 vLLM and SGLang configurations, including forced scheduler-pressure and disaggregated handoff lifecycles. It is used when validating replay…

Drives persistent Claude Code, Codex, or OpenCode agent sessions through a Dynamo OpenAI/Anthropic-compatible endpoint over Agent Client Protocol (ACP). Use when an agent must delegate a bounded task to another coding-agent harness running a model served by…

Adds, updates, moves, or removes content on the Dynamo Fern docs site — standard docs pages, catalog-driven recipe and feature-benchmark pages, examples, recipes, and translations — keeping everything in line with the documentation style guide. Use for any…

Knowledge of Fern's built-in MDX component library (accordions, callouts, cards, steps, tabs, code blocks, API-reference snippets, and more) for authoring docs pages. Use when writing or editing a Fern `.mdx` page and deciding whether a component would…

Create self-contained interactive HTML code-review dashboards from GitHub or GitLab pull requests, checked-out branch diffs, or supplied unified diffs, with correctness and safe-to-merge scores, smell indicators, interactive component and finding diagrams,…

Knowledge of Fern's site-level navigation and structure configuration — how a docs site is organized in `docs.yml` (and product/version `.yml` files) using sections, pages, folders, tabs, tab variants, versions, products, changelogs, and site-level settings,…

Sets up a structured debugging session for a Dynamo bug — pull the report from a Linear ticket, GitHub issue, or pasted text, capture the environment, create a persistent worklog markdown file, and walk through rebuild, reproduction, and investigation steps.…

Creates or updates Dynamo Enhancement Proposals as GitHub issues, including lightweight DEPs, implementation plans, and retroactive DEPs for ai-dynamo/dynamo. Use when proposing a feature or architecture change, drafting an implementation plan, or filing a…

Use when a user asks to tune, optimize, accelerate, or deploy a PyTorch model or inference pipeline for GPU performance.

Use when measuring inference performance of a PyTorch model before and after AITune optimization — capturing baseline, compilation time, tuned throughput, and speedup.

Use when creating a git commit to ensure the message follows the Conventional Commits specification

Run full documentation sync (not limited to recent git changes)

Use when inspecting a PyTorch model or pipeline to identify tunable submodules, detect dynamic shapes, and determine the recommended tuning mode before optimization.

Use when creating a new release tag for the project

Use when verifying that a tuned model's outputs match the baseline eager model within numerical tolerance — run after each backend compilation before accepting it as a deployment candidate.

Review the current branch against origin/main, capture findings in artifacts/code-review.md as a living document, validate every finding against the actual code, reproduce confirmed issues with the aiperf CLI against the in-repo mock server, and draft inline…

This skill should be used when the user asks to "create a linear issue", "add a linear issue", "track this in linear", "open a linear ticket", "log this in linear", "create a ticket", or mentions creating an issue for tracking work in Linear.

Use when reviewing AIPerf code for the SEMANTIC qualities that mechanical linters cannot catch — error-message informativeness, type-hint descriptiveness, docstring example usefulness, naming disambiguation, convention explicitness, comment density, and…

Bump the AIPerf package version (e.g. 0.10.0 -> 0.11.0) across pyproject.toml, the mock server, and the version strings shown in docs, then open a PR to main

Cherry-pick a commit from origin/main into a release branch using a temporary git worktree

Migrate a plain Markdown docs/ folder to a Fern documentation site from scratch. Use this skill when a project has no existing publishing framework and needs to scaffold Fern config, migrate content, and build navigation.

Convert InferenceX DB config/benchmark pairs, Dynamo recipe YAML, or confirmed custom serving configs into validated aiconfigurator estimate requests. Use when adapting server configuration, checking whether a recipe can be estimated, generating canonical…

Use when validating AIC predictions against real GPU serving measurements and root-causing fidelity gaps (prediction-vs-measured issues, memory/concurrency mismatches, per-op latency divergence). Covers dummy-weight methodology, measurement parity traps,…

Use when running long AIC/aiconfigurator GPU perf auto-collection for a specific GPU/framework/framework_version, including draft-PR checkpoints, resumable collect_xx.py runs, framework-version/kernel preflight, collector fixes/skips, special runtime images,…

Design, add, review, or modify AIC Collector operations and their case population. Use for new Collector ops, backend registry entries, collector/cases YAML, case generators/getters, pruning or deduplication, persisted perf keys, framework-version routing,…

Audit async-openai dependency upgrades in ai-dynamo/frontend-crates and Dynamo consumers. Use when bumping async-openai, reviewing an upgrade PR, checking whether locally owned or vendored OpenAI protocol types can be deleted or narrowed, detecting drift in…

Resolve sync drift between frontend-crates and ai-dynamo/dynamo. Pulls lib/{protocols,renderer} src+tests up to dynamo main, fixes the diverged-Cargo.toml breakage the new code introduces, verifies the full CI gate, and opens a sync PR. Use when sync-check…

Generate optimized tool call parsers for the dynamo-parsers crate from HuggingFace model chat templates. Use this when you need to add support for a new model's tool calling format. Takes a HuggingFace model name, analyzes its chat template, compares with…

Interactive guide for authoring or updating a Grove user guide. Leads a structured dialogue with the user, section by section, and writes the documentation incrementally as each section is confirmed. Also supports continuing work on an existing user guide.…

Interactive guide for authoring a Grove Enhancement Proposal (GREP). Leads a structured dialogue with the user, section by section, and writes the proposal file incrementally as each section is confirmed. Also supports continuing work on an existing GREP.

Use for source-matched NIXL Python API help on agents, descriptors, metadata, transfers, polling, or cleanup. Do NOT use for install/framework setup.

Drive the real Pi CLI headlessly against a Dynamo or OpenAI-compatible endpoint for pi-dynamo-provider validation. Use when testing Pi provider installs, session header tracing, Pi subagent runs, saved traces, or parent/child session behavior without manually…