Agent skill

Uplift Ttsim CI

by tenstorrent in tenstorrent/tt-mlir

Uplift the TTSim version used by tt-mlir CI and refresh WH/BH simulator skips.

Apache-2.0Auto-check passedTesting & QA

Install Uplift Ttsim CI

skills CLI
$ npx skills add tenstorrent/tt-mlir --skill uplift-ttsim-ci -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install tenstorrent/tt-mlir uplift-ttsim-ci --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/tenstorrent/tt-mlir.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/uplift-ttsim-ci .claude/skills/uplift-ttsim-ci && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
uplift-ttsim-ci
GitHub stars
314
Token cost
~3.1k tokens
SKILL.md length
1,262 words
Files
1
Skills in repo
9
Repo updated
First seen
Licence
Apache-2.0

At a glance

Uplift the TTSim version used by tt-mlir CI and refresh WH/BH simulator skips.

  • Works in 7 steps: Freeze the Release and CI Scope → Create Independent WH/BH Environments → Plan the Unskip Matrix → …
  • Updating .github/workflows/call-test-ttsim.yml
  • SKILL.md covers Definition of Done, Ground Rules, Stage 1: Freeze the Release… and Stage 2: Create Independent…, plus 5 more sections
  • Calls python, git and curl; reaches github.com

What it does

Uplift Ttsim CI is an agent skill from tenstorrent/tt-mlir. Uplift the TTSim version used by tt-mlir CI and refresh WH/BH simulator skips. Use when updating .github/workflows/call-test-ttsim.yml, changing ttsim-version, validating WH or BH TTSim golden tests, or triaging simulator-specific pytest skips.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Testing & QA, covering Unit testing. It works with pytest and GitHub Actions. The repository describes itself as: Tenstorrent MLIR compiler. The licence is Apache-2.0.

When your agent uses it

  • Updating .github/workflows/call-test-ttsim.yml
  • Changing ttsim-version
  • BH TTSim golden tests
  • Triaging simulator-specific pytest skips

Example prompts

  • “/uplift-ttsim-ci”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Freeze the Release and CI Scope
  2. Create Independent WH/BH Environments
  3. Plan the Unskip Matrix
  4. Run Every Candidate in Isolation
  5. Restore Triage State
  6. Run the Full Regression Sweep
  7. Final Verification and Handoff

What it can do on your machine

Read from SKILL.md and the folder at commit 78b7044. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python
    • git
    • curl
    • cmake
    • rg

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Uplift Ttsim CI loads about 3.1k tokens when it runs. Until then it costs about 66 tokens; SKILL.md has 1,262 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~66
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from tenstorrent/tt-mlir at commit 78b7044, republished under its Apache-2.0 licence (© tenstorrent). 1,262 words, ~3,084 tokens.

Download SKILL.mdSave it as .claude/skills/uplift-ttsim-ci/SKILL.md (or your agent's skills folder).
name
uplift-ttsim-ci
description
Uplift the TTSim version used by tt-mlir CI and refresh WH/BH simulator skips. Use when updating `.github/workflows/call-test-ttsim.yml`, changing `ttsim-version`, validating WH or BH TTSim golden tests, or triaging simulator-specific pytest skips.

Uplift TTSim CI

Follow the stages below in order. Do not move the regression sweep ahead of unskip triage, and do not stop after unskip triage.

Definition of Done

An uplift is complete only when all of these are true:

  1. The release tag and both WH/BH assets are verified.
  2. Every applicable simulator-skip node is inventoried before testing.
  3. Every executable candidate is run in its own pytest process on each applicable architecture, and the skip edits are resolved.
  4. The temporary noop edit is restored.
  5. Every workflow-listed file is run, one file per pytest process, on both WH and BH after the skip edits.
  6. Changed skip files are rerun on both architectures, the workflow is syntax-checked, and the final diff contains no triage-only edits.

If any stage is incomplete, report the coverage gap; do not call the uplift done.

Ground Rules

  • .github/workflows/call-test-ttsim.yml is the source of truth for the test list, matrix, descriptors, and assets. Re-read it at the start; never reuse a stale test list copied into this skill.
  • Cover wormhole_b0 and blackhole. Ignore Quasar/QSR unless requested.
  • A TTSim error may terminate pytest with no summary, sometimes with exit code 1 rather than a signal-derived code. During unskip triage, one exact node per pytest process is mandatory. A whole-file run with skips disabled is not valid unskip evidence.
  • Serialise candidate nodes within one architecture. WH and BH may run in parallel because they have separate simulator homes, libraries, descriptors, artifact paths, and logs.
  • Use /opt/ttmlir-toolchain/venv/bin/python -m pytest; the local pytest wrapper may hide useful output.
  • Before local commands, activate without set -u; activation expects some variables to be unset:
bash
unset BUILD_DIR
source env/activate
export BUILD_DIR="$PWD/build"

Stage 1: Freeze the Release and CI Scope

  1. Find the latest public release of tenstorrent/ttsim.
  2. Verify that it contains both libttsim_wh.so and libttsim_bh.so.
  3. Record the release tag, publication time, asset names, current tt-mlir SHA, and current tt-metal SHA.
  4. Record the initial git status --short; preserve unrelated user changes.
  5. Extract the exact .py arguments from the Run golden pytest on TTSim step into a shell TEST_FILES array and print it for review:
bash
mapfile -t TEST_FILES < <(
  awk '/^[[:space:]]+pytest /,/--sys-desc/ {
    if ($1 ~ /\.py$/) print $1
  }' .github/workflows/call-test-ttsim.yml
)
((${#TEST_FILES[@]} > 0)) || {
  echo "No TTSim workflow tests found"
  exit 1
}
printf '%s\n' "${TEST_FILES[@]}"
  1. Update only the workflow's ttsim-version default unless the current release requires a justified matrix/setup change. Keep this matrix:
yaml
- arch: wormhole_b0
  soc_desc: wormhole_b0_80_arch.yaml
  ttsim_asset: libttsim_wh.so
- arch: blackhole
  soc_desc: blackhole_140_arch.yaml
  ttsim_asset: libttsim_bh.so

Stage 2: Create Independent WH/BH Environments

Use a versioned root and separate result directories:

bash
export TTSIM_VERSION=vX.Y.Z
export TTSIM_ROOT="${TMPDIR:-/tmp}/ttmlir-ttsim/$TTSIM_VERSION"
mkdir -p \
  "$TTSIM_ROOT/wormhole_b0" \
  "$TTSIM_ROOT/blackhole" \
  "$TTSIM_ROOT/results/wormhole_b0" \
  "$TTSIM_ROOT/results/blackhole"

curl -L --fail --retry 3 \
  -o "$TTSIM_ROOT/wormhole_b0/libttsim_wh.so" \
  "https://github.com/tenstorrent/ttsim/releases/download/$TTSIM_VERSION/libttsim_wh.so"
curl -L --fail --retry 3 \
  -o "$TTSIM_ROOT/blackhole/libttsim_bh.so" \
  "https://github.com/tenstorrent/ttsim/releases/download/$TTSIM_VERSION/libttsim_bh.so"

cp third_party/tt-metal/src/tt-metal/tt_metal/soc_descriptors/wormhole_b0_80_arch.yaml \
  "$TTSIM_ROOT/wormhole_b0/soc_descriptor.yaml"
cp third_party/tt-metal/src/tt-metal/tt_metal/soc_descriptors/blackhole_140_arch.yaml \
  "$TTSIM_ROOT/blackhole/soc_descriptor.yaml"

Override TTSIM_ROOT with a durable path when logs must survive a reboot, but keep generated binaries, artifacts, and reports outside the git working tree.

Set common variables once:

bash
export TT_MLIR_HOME="$PWD"
export TT_METAL_HOME="$PWD/third_party/tt-metal/src/tt-metal"
export TT_METAL_SLOW_DISPATCH_MODE=1
export TT_METAL_DISABLE_SFPLOADMACRO=1
export TT_METAL_INSPECTOR=0
export TT_METAL_INSPECTOR_RPC=0
export LD_LIBRARY_PATH="$PWD/build/lib:${TTMLIR_TOOLCHAIN_DIR}/lib:${LD_LIBRARY_PATH:-}"

Build the current checkout before testing; results from stale tt-mlir or tt-metal binaries are invalid:

bash
cmake --build "$BUILD_DIR"

Generate and retain one system descriptor per architecture. These commands may run in parallel:

bash
export TT_METAL_SIMULATOR_HOME="$TTSIM_ROOT/wormhole_b0"
export TT_METAL_SIMULATOR="$TT_METAL_SIMULATOR_HOME/libttsim_wh.so"
ttrt query --save-artifacts \
  --artifact-dir "$TT_METAL_SIMULATOR_HOME/ttrt-artifacts" --quiet

export TT_METAL_SIMULATOR_HOME="$TTSIM_ROOT/blackhole"
export TT_METAL_SIMULATOR="$TT_METAL_SIMULATOR_HOME/libttsim_bh.so"
ttrt query --save-artifacts \
  --artifact-dir "$TT_METAL_SIMULATOR_HOME/ttrt-artifacts" --quiet

Do not share TT_METAL_SIMULATOR_HOME, --sys-desc, --path, or result logs between architectures.

Stage 3: Plan the Unskip Matrix

Complete the candidate plan before executing candidates.

  1. Search only the workflow-listed files for sim skip sites. Record file, line, mark, parameter scope, and intended architecture in sim-mark-sites.txt.
bash
rg -n '\bsim\b' "${TEST_FILES[@]}" > sim-mark-sites.txt
  1. With the normal environment detection ("sim"), collect exact skipped node IDs on WH and BH without executing tests:
bash
COLUMNS=10000 /opt/ttmlir-toolchain/venv/bin/python -m pytest \
  --setup-plan -vv --color=no "${TEST_FILES[@]}" \
  --sys-desc "$TT_METAL_SIMULATOR_HOME/ttrt-artifacts/system_desc.ttsys" \
  2>&1 |
  awk '/ SKIPPED/ {sub(/[[:space:]]+SKIPPED.*/, ""); print}' |
  LC_ALL=C sort -u

Save this once per architecture as all-skipped-before-noop.txt.

  1. Temporarily change _get_current_environment() in test/python/golden/conftest.py to return "noop" when TT_METAL_SIMULATOR is set. Do not add noop to ALL_ENVIRONMENTS. Simulator marks no longer match, while unconditional skips, non-simulator skips, and only_config behavior remain active.
  2. Repeat the setup-plan collection into still-skipped-with-noop.txt.
  3. Both files must be sorted. Produce the executable candidate list with:
bash
comm -23 \
  all-skipped-before-noop.txt \
  still-skipped-with-noop.txt \
  > candidates.txt

Review every source mark against the resulting WH/BH lists. Record sim-marked nodes that remain skipped under noop in not-executable.txt; do not count them as tested or remove their simulator mark without other evidence. Freeze the two candidate lists before execution and record their counts.

Show full SKILL.md (654 more words)Show less

Stage 4: Run Every Candidate in Isolation

Keep the noop edit active. For each architecture, run the corresponding candidates.txt serially. Quote each complete node ID:

bash
timeout --signal=TERM --kill-after=10s 180s \
  env PYTHONUNBUFFERED=1 \
  /opt/ttmlir-toolchain/venv/bin/python -m pytest -svvv "$node_id" \
  --sys-desc "$TT_METAL_SIMULATOR_HOME/ttrt-artifacts/system_desc.ttsys" \
  --path "$TT_METAL_SIMULATOR_HOME/pytest_artifacts_candidates" \
  --tb=short -rs

Write one log per node plus a TSV/JSON manifest containing architecture, node ID, elapsed time, exit code, and result. Continue after nonzero exits and print progress after every node. WH and BH loops may run concurrently, but never group multiple node IDs into one pytest invocation. Keep -s enabled so pytest capture cannot swallow the last TTSim error when the process exits.

Classify results from both exit status and the pytest terminal summary:

  • passed: pytest reports the node passed. This is the only automatic unskip evidence.
  • failed: pytest prints a normal failure/error summary.
  • aborted: the process ends without a pytest terminal summary, even if its exit code is only 1.
  • timed_out: the 180-second timeout expires.
  • skipped, xfailed, or xpassed: record separately; none proves a clean simulator pass.

For an empty or unclear abort log, rerun that exact node with PYTHONUNBUFFERED=1 -svvv. Do not rerun a group.

After the matrix is complete:

  • Remove a simulator mark only when every architecture to which it applies passed.
  • Narrow mixed results to the smallest dtype, shape, op, or architecture scope. Single-chip WH is n150; single-chip BH is p150.
  • If a mark on one parametrization axis mixes passing and failing combinations from another axis, restructure the parametrization so the mark names the exact combinations; do not retain the broad axis-level skip.
  • A config list is AND'd; separate groups are OR'd. skip_config(["n150", "sim"]) means WH simulator only, while SkipIf("n150", "sim") means n150 or any simulator.
  • Preserve unrelated skips. For example, if only sim is obsolete in SkipIf("ttnn", "emitc", "emitpy", "sim"), keep SkipIf("ttnn", "emitc", "emitpy").

If behavior differs from the previous TTSim version, run the smallest failing node against both versions with the same tt-mlir checkout and corresponding system descriptors.

Stage 5: Restore Triage State

Restore _get_current_environment() to return "sim" before any final validation. Inspect the diff rather than using a destructive checkout, and verify that conftest.py has no triage-only change. Keep the candidate manifests as validation artifacts, not source changes.

Stage 6: Run the Full Regression Sweep

This stage is mandatory and happens after skip updates and noop restoration.

Run every file in TEST_FILES, one file per pytest process, on WH and BH. Do not use one aggregate pytest command: an abort would hide the remaining files. Continue after failures. The two architecture loops may run in parallel.

bash
timeout --signal=TERM --kill-after=15s 3600s \
  env PYTHONUNBUFFERED=1 \
  /opt/ttmlir-toolchain/venv/bin/python -m pytest -vv "$test_file" \
  --sys-desc "$TT_METAL_SIMULATOR_HOME/ttrt-artifacts/system_desc.ttsys" \
  --path "$TT_METAL_SIMULATOR_HOME/pytest_artifacts_regression" \
  --tb=short -rs

Some files may take more than 30 minutes, but an individual node should not take more than about three minutes. Monitor each live -vv log at least every three minutes. If progress stops, terminate the file run, isolate the last reported node with the 180-second candidate command, update the smallest necessary skip, and rerun that file. A generous file timeout is not a substitute for progress monitoring.

Record exactly one final status per file per architecture:

  • passed: pytest exits 0 with a terminal summary; expected skips/xfails are allowed.
  • failed: pytest reports normal failures.
  • aborted: no pytest terminal summary, regardless of exit code.
  • timed_out: the file timeout expires.

Any skip change made during regression requires a fresh run of that file on both architectures. Do not finish with an untriaged failed, aborted, or timed out file.

Stage 7: Final Verification and Handoff

  1. Run focused WH and BH pytest for every file whose skips changed.
  2. Verify all workflow-listed files have a final WH and BH status.
  3. Syntax-check .github/workflows/call-test-ttsim.yml with available local tooling.
  4. Run git diff --check and inspect git status and the complete diff.
  5. Confirm conftest.py is restored and generated simulator assets/results are not in the source diff.
  6. Summarize:
    • release tag and verified asset names;
    • descriptor YAML and generated .ttsys used per architecture;
    • candidate counts and result totals per architecture;
    • simulator skip removals, additions, and narrowings;
    • per-file regression status for WH and BH;
    • focused reruns and workflow validation;
    • any explicit remaining coverage gap.

© tenstorrent, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/uplift-ttsim-ci of tenstorrent/tt-mlir.

Open the folder on GitHubat commit 78b7044

Compare with similar skills

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Categories

Questions about Uplift Ttsim CI

What does Uplift Ttsim CI do?

Uplift the TTSim version used by tt-mlir CI and refresh WH/BH simulator skips. Uplift Ttsim CI is an agent skill from tenstorrent/tt-mlir. Uplift the TTSim version used by tt-mlir CI and refresh WH/BH simulator skips.

When should I use Uplift Ttsim CI?

Uplift Ttsim CI fits situations like: updating .github/workflows/call-test-ttsim.yml; changing ttsim-version; BH TTSim golden tests; triaging simulator-specific pytest skips.

How do I install Uplift Ttsim CI in Claude Code?

Run `npx skills add tenstorrent/tt-mlir --skill uplift-ttsim-ci -a claude-code`. Or copy the skill folder (.claude/skills/uplift-ttsim-ci in tenstorrent/tt-mlir) into .claude/skills/uplift-ttsim-ci in your project. Claude Code loads it when a task matches its description.

How do I install Uplift Ttsim CI in Codex?

Run `npx skills add tenstorrent/tt-mlir --skill uplift-ttsim-ci -a codex`. Or copy the skill folder (.claude/skills/uplift-ttsim-ci in tenstorrent/tt-mlir) into .agents/skills/uplift-ttsim-ci in your project. Codex loads it when a task matches its description.

Can I use Uplift Ttsim CI in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add tenstorrent/tt-mlir --skill uplift-ttsim-ci -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/uplift-ttsim-ci, .gemini/skills/uplift-ttsim-ci, .github/skills/uplift-ttsim-ci and .opencode/skills/uplift-ttsim-ci in your project.

What does Uplift Ttsim CI need to run?

Going by SKILL.md and its folder, Uplift Ttsim CI needs the command-line tools its instructions call (python, git, curl, cmake and rg). Our summary lists: Python 3.

Does Uplift Ttsim CI access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Uplift Ttsim CI safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Uplift Ttsim CI use?

Uplift Ttsim CI is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Uplift Ttsim CI use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Uplift Ttsim CI?

Skills that share tags, products or a category with Uplift Ttsim CI: Simple Modern Uv (jlevy/simple-modern-uv, 301 stars), Megatron Core Testing Guide (NVIDIA/Megatron-LM, 18k stars), Py Package Check (ipea/geobr, 961 stars) and Gating Deid Leakage (maziyarpanahi/openmed, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Uplift Ttsim CI?

tenstorrent (a GitHub organization) maintains it in tenstorrent/tt-mlir, which has 314 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 10, 2026.

Source: tenstorrent/tt-mlir on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.