tenstorrent

Write a ForgeModel-compatible loader for a HuggingFace model, validate it on CPU, and push the result to a branch on tenstorrent/tt-forge-models.

Install tt-forge, run the model loader from the cpu bringup branch on Tenstorrent hardware, iterate on failures, and open a PR to tenstorrent/tt-forge-models on success.

File a bug report with a reproducer against Tenstorrent repos (tt-lang, tt-metal, tt-xla)

Set up and verify remote connection to Tenstorrent hardware. Provides tools for running kernels, copying files, and reading logs on remote devices.

TTNN trace capture and replay for eliminating dispatch overhead. Essential for real-time inference and multi-chip performance.

Profile and optimize TT-Lang kernels for performance. Covers auto-profiling, perf summary, signposts, and optimization workflow.

Comprehensive TT-Lang DSL reference including programming model, APIs, hardware constraints, and guides for translating CUDA, Triton, PyTorch, or TTNN kernels

TTNN operations library reference for Tenstorrent hardware. Covers tensor APIs, ops catalog, model conversion from PyTorch, and memory/layout configuration.

Triage one tt-forge-models training test failing with a bfloat16 dtype-mismatch RuntimeError (e.g. "mat1 and mat2 must have the same dtype, but got Float and BFloat16", "'<op>' not implemented for 'BFloat16'"). For cross-dtype operands, attempts a minimal loader fix propagating `dtype_override` into the offending tensor constructor, then re-runs CPU + pytest and updates the YAML (passing -> EXPECTED_PASSING; new failure -> KNOWN_FAILURE_XFAIL). For op-not-implemented (no PyTorch kernel), goes straight to KNOWN_FAILURE_XFAIL with the verbatim error. Updates every training entry sharing the affected loader. Never edits inference YAML or `dynamic_loader.py`.

Triage one tt-forge-models training test stuck at FAILED_FE_COMPILATION with reason "tt-forge-models doesn't implement unpack_forward_output for this model." Inspects the model's forward output, registers a handler or writes a per-loader override, and updates the YAML.

Analyze CI benchmark workflow runs from GitHub Actions for the tt-xla project. Produces a markdown report covering failed jobs (with root-cause error extraction via logs and Glean), successful model performance metrics (samples/sec, TTFT, device perf), perf regressions/improvements vs previous nightly, and the full dependency commit chain (tt-xla, tt-mlir, tt-metal). Use this skill whenever the user wants to analyze a CI run, review nightly benchmark results, investigate CI failures, check benchmark performance from a workflow run, or asks about "latest nightly" results. Also trigger when the user pastes a GitHub Actions run URL or mentions a run ID in the context of performance analysis, or asks about perf regressions.

Use when auditing a TTNN model's IR for missed op fusion opportunities — both direct TTNN fusions (a fused ttnn op already exists) and theoretical fusions (the pattern is a single kernel in torch/triton/cuda)

Analyze a GitHub Actions run and summarize failures

Analyzes, debugs and proposes fixes for graph breaks in PyTorch/XLA model compilation. Use when a model generates more graphs than expected during compilation, the user mentions "graph break", or when debugging excessive graph generation in tt-xla pipelines.

Code review skill specialized for tt-xla (Python + C++ PJRT plugin for Tenstorrent hardware). Covers C++ memory safety, PJRT API patterns, Python test standards, and project-specific conventions.

Create Excalidraw diagram JSON files that make visual arguments. Use when the user wants to visualize workflows, architectures, or concepts.

How to add a new operation (op) to the tt-mlir compiler across all layers: TTIR/TTNN dialect definitions, StableHLO composite conversion, TTIR-to-TTNN conversion, EmitC/EmitPy conversions, flatbuffer schema and serialization, runtime implementation, OpModel, ttir_builder, golden functions, and all associated tests. Use this skill whenever the user asks to add an op, implement an op, create a new operation, add support for a TTNN op, or mentions adding an op to the compiler pipeline. Also trigger when the user wants to know what files to change for a new op, or asks about the op-adding workflow.

Given a `.mlir` file (or a directory of `.mlir` files) with TTIR ops, run the same TTIR normalization passes as `D2MFrontendPipeline` before D2M, then produce per-file outputs: `preprocessed.mlir`, `ttir-op-report.txt` (op counts from normalized IR), and `ops.mlir` (one func per unique op configuration, golden-style). Optional: per-pass IR dumps.

Add a new composite op decomposition pattern to the TTMetal pipeline. Use when the user wants to decompose/lower a high-level TTIR op (e.g. rms_norm, sdpa, layer_norm, softmax) into primitive TTIR ops (matmul, add, multiply, etc.) for the D2M/TTMetal backend. Also trigger when the user mentions "decomposition pattern", "decompose op for ttmetal", or "lower op to primitives".

Compile and optionally execute every func.func in an ops.mlir-style snippet file (or every .mlir file in a directory) using `run_ops_mlir_snippets.py`. Use when the user wants to compile or run TTIR op snippets on device, test ops.mlir files, or check which ops compile/execute successfully.

Elementwise TTIR→D2M→TTMetal path: tablegen, TTIRToD2M.cpp, D2MToTTKernel.cpp, and — only when the kernel API callee is new — TTKernelIncludesMap.h (per-op api/compute/eltwise_unary/*.h mapping for JIT). Does not edit D2MGenericRegionOps.cpp or TTKernelToCpp.cpp. Not for reductions, matmul, views, or CCL.

Validate a tt-mlir PR against tt-xla by creating a cherry-picked branch and triggering CI. Invoked as: /validate-tt-mlir-against-tt-xla <PR number or URL>. Use this skill whenever the user wants to test, validate, qualify, or check a tt-mlir PR in tt-xla, or mentions running uplift qualification test suite, or asks to trigger tt-xla CI for a tt-mlir change. Also triggers when the user mentions "xla validate", "xla test", or "validate in xla".

Add full builder API support (@tag, @parse, @split) for a TTIR op. Use this skill whenever the user wants to add builder support for a new TTIR op, upgrade an existing _op_proxy-based op to use @tag/@parse/@split decorators, or asks about how to add builder API for an op in ttir_builder.py. Also trigger when the user mentions adding tag/parse/split for an op, or wants to make an op work with the parse/split test infrastructure.

Run LLK tests using the test runner agent. Never run pytest directly.

Look up Tensix architecture, instruction, or LLK implementation details across architectures. Orchestrates sage agents in parallel.

Debug compilation or runtime errors in LLK kernels. Infers architecture and kernel type from path.

Get structured porting guidance when moving a kernel between architectures. Launches sages for source and target, reads test harness.

Enforce safe bash scripting practices when writing, reviewing, or fixing shell scripts. Covers quoting, arrays, conditionals, arithmetic, redirections, strict mode, and static analysis. Use when editing .sh/.bash files, reviewing shell scripts, fixing shellcheck warnings, or writing new bash code.

Guides test-first workflows for bugs and features: write a minimal failing test that encodes contracts and clear failure messages, confirm the gap is not already covered, implement and document the fix, then verify green. Use when fixing bugs with tests, adding features via TDD, writing regression tests, or when the user asks for red-green-refactor or test-driven development.

Capture on-CPU and off-CPU flamegraphs of the running tt_media_server_cpp main (Drogon) and worker processes using Linux perf + Brendan Gregg's FlameGraph. Use when the user asks to profile the C++ server, find a performance bottleneck, identify slow code paths, capture a flamegraph, or investigate CPU usage / lock contention in BlazeRunner or the decode scheduler.

Trigger Tenstorrent Blaze media server and tt-metal upstream Docker image builds with gh. Use when the user asks to build Blaze and Metal Docker images, run the tt-shield media server image workflow, or build tt-metal images from the tt-llm-engine submodule reference.

Delegates LLK test runs to the llk-test-runner agent using @.cursor/agents/llk-test-runner.md. Use when the user asks to run tests or mentions LLK tests. Ensure test commands run to completion before reading terminal output (no polling).

Run device performance benchmarks for tt-blacksmith single-chip training workloads. Use when the user asks to benchmark, profile, or measure performance of a training workload on Tenstorrent hardware, or mentions tracy, tt-perf-report, or device time analysis.

Rebases this Tenstorrent fork of terraform-provider-netbox onto the latest e-breuninger upstream and cuts a new release. Use when the user asks to rebase onto upstream, sync with upstream, pull in upstream changes, update from e-breuninger, or release a new version of the fork.