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Agent skill

Check Runtime Parity

by ayutaz in ayutaz/piper-plus

推論パスの canonical Python (exportonnx.py / vits/models.py:VitsModel.infer) を変更した PR で、6 ランタイム (Python runtime / Rust / Go / C / C++ / WASM) の inference path が追随しているかを git diff で確認。PR

MITAuto-check passedAI & LLM Engineering

Install Check Runtime Parity

skills CLI
$ npx skills add ayutaz/piper-plus --skill check-runtime-parity -a claude-code

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

GitHub CLI
$ gh skill install ayutaz/piper-plus check-runtime-parity --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/ayutaz/piper-plus.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/check-runtime-parity .claude/skills/check-runtime-parity && 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
check-runtime-parity
GitHub stars
220
Token cost
~972 tokens
SKILL.md length
227 words
Files
1
Skills in repo
20
Repo updated
First seen
Licence
MIT

At a glance

推論パスの canonical Python (exportonnx.py / vits/models.py:VitsModel.infer) を変更した PR で、6 ランタイム (Python runtime / Rust / Go / C / C++ / WASM) の inference path が追随しているかを git diff で確認。PR

  • Works in 4 steps: canonical ファイルが触られているか確認 → 他 6 ランタイムの inference path も触られているか確認 → ONNX 入出力名の変更ならスキーマ検査も走らせる → …
  • AI & LLM Engineering work in your project
  • SKILL.md covers 何をチェックするか, 実行手順, 確認すべき事項 and トラブルシューティング, plus 1 more section
  • Calls python and git

What it does

Check Runtime Parity is an agent skill from ayutaz/piper-plus. 推論パスの canonical Python (exportonnx.py / vits/models.py:VitsModel.infer) を変更した PR で、6 ランタイム (Python runtime / Rust / Go / C / C++ / WASM) の inference path が追随しているかを git diff で確認。PR

Its SKILL.md is about 970 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 AI & LLM Engineering. It works with ONNX, Python, C++ and WebAssembly. The repository describes itself as: Multilingual neural TTS (6 languages: JA/EN/ZH/ES/FR/PT, code supports SV) — C++, C, Rust, Go, Python, npm (WASM). VITS + Prosody, streaming, CUDA/CoreML/DirectML. pip install…. The licence is MIT.

When your agent uses it

  • AI & LLM Engineering work in your project

Example prompts

  • “/check-runtime-parity”

Requirements

  • Python 3
  • Docker
  • Pre-approved tools (allowed-tools): Bash(git diff *), Bash(git status *), Bash(grep *), Bash(ls *)

Workflow steps

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

  1. canonical ファイルが触られているか確認
  2. 他 6 ランタイムの inference path も触られているか確認
  3. ONNX 入出力名の変更ならスキーマ検査も走らせる
  4. 必要なら他ランタイムを touch する PR を分割

What it can do on your machine

Read from SKILL.md and the folder at commit 9c6946e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(git diff *)
    • Bash(git status *)
    • Bash(grep *)
    • Bash(ls *)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python
    • git

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

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    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

Check Runtime Parity loads about 972 tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 227 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~972

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 ayutaz/piper-plus at commit 9c6946e, republished under its MIT licence (© ayutaz). 227 words, ~972 tokens.

Download SKILL.mdSave it as .claude/skills/check-runtime-parity/SKILL.md (or your agent's skills folder).
name
check-runtime-parity
description
推論パスの canonical Python (`export_onnx.py` / `vits/models.py:VitsModel.infer`) を変更した PR で、6 ランタイム (Python runtime / Rust / Go / C# / C++ / WASM) の inference path が追随しているかを git diff で確認。PR
allowed-tools
Bash(git diff *), Bash(git status *), Bash(grep *), Bash(ls *)
disable-model-invocation
true
<!-- editorconfig-checker-disable-file -->

ランタイム推論 parity チェック

src/python/piper_train/export_onnx.py か src/python/piper_train/vits/models.py の VitsModel.infer (ONNX グラフ入出力定義) を変更した PR で、他 5 ランタイムの inference path も同じ変更を反映しているかを PR 提出前に 確認する。

何をチェックするか

PR #391 → PR #443 の事故パターン:

  • PR #391 は Python ランタイムだけ speaker_embedding 形状を修正
  • 他 5 ランタイム (Rust / Go / C# / C++ / WASM 推論) は古い形状のまま放置
  • PR #443 で気付くまで silent regression 状態

このパターンを検出するため、カノニカルファイル が触られている PR で 他ランタイムの inference path も同 PR で触れているかを確認する:

Canonical (Python)対応する他ランタイム file
src/python/piper_train/export_onnx.py(ONNX export 自体は Python 専用、ただし出力スキーマを変える場合は下記の loader も更新必要)
src/python/piper_train/vits/models.py:VitsModel.infersrc/python_run/piper_plus/voice.py (Python runtime)
ONNX グラフ入出力名 (speaker_embedding / prosody_features / language_id)src/rust/piper-core/src/engine.rs (Rust)
同上src/go/piperplus/synth.go (Go)
同上src/csharp/PiperPlus.Core/Inference/PiperSession.cs (C#)
同上src/cpp/piper_plus.cpp (C++)
同上src/wasm/openjtalk-web/src/index.js または piper-wasm (WASM)

実行手順

1. canonical ファイルが触られているか確認
bash
CANONICAL_TOUCHED=$(git diff --name-only origin/dev...HEAD | \
  grep -E '^(src/python/piper_train/(export_onnx|vits/models)\.py)$')

if [ -z "$CANONICAL_TOUCHED" ]; then
  echo "canonical inference file は変更されていない — このチェックは skip 可"
  exit 0
fi

echo "Canonical 変更検出: $CANONICAL_TOUCHED"
2. 他 6 ランタイムの inference path も触られているか確認
bash
RUNTIME_FILES=(
  "src/python_run/piper_plus/voice.py"
  "src/rust/piper-core/src/engine.rs"
  "src/go/piperplus/synth.go"
  "src/csharp/PiperPlus.Core/Inference/PiperSession.cs"
  "src/cpp/piper_plus.cpp"
  "src/wasm/openjtalk-web/src/index.js"
)

echo "=== 他ランタイム inference path の touch 状況 ==="
for f in "${RUNTIME_FILES[@]}"; do
  if git diff --name-only origin/dev...HEAD | grep -q "^${f}$"; then
    echo "  TOUCHED  $f"
  else
    echo "  UNTOUCHED $f  ← 追随必要かもしれない"
  fi
done
3. ONNX 入出力名の変更ならスキーマ検査も走らせる

speaker_embedding / prosody_features / language_id 等の入出力名を追加・ 削除・改名した場合は以下も実行:

bash
# ONNX 入力契約の検査 (PR #443 で導入)
python scripts/check_onnx_inputs.py --strict

# 既存 parity gate
python scripts/check_voice_catalog_parity.py
python scripts/check_inference_input_contract.py
4. 必要なら他ランタイムを touch する PR を分割

UNTOUCHED な runtime が多いなら、1 PR で全 6 ランタイムを揃える か、 「Python canonical → 他ランタイム追随」を 1 issue でリンク にする。 silent regression を残さない。

確認すべき事項

  • canonical Python の変更が ONNX 入出力グラフに影響する変更か (形状 / 名前 / dtype の変更) を把握
  • 影響あるなら他 6 ランタイム inference path も同 PR で更新したか
  • scripts/check_onnx_inputs.py --strict が pass
  • scripts/check_voice_catalog_parity.py が pass
  • scripts/check_inference_input_contract.py が pass
  • PR description に「Python canonical 修正 + N 他ランタイム追随」を明記

トラブルシューティング

症状対処
canonical を触ったが他 runtime は触れていない「ONNX グラフは変えていない (内部リファクタのみ)」なら OK。グラフを変えたら他 runtime も追随必要
check_onnx_inputs.py --strict が fail出力モデルの入出力 ports が docs/spec/inference-input-contract.toml と drift
他 runtime を全部触るのが大きすぎるPython と Rust/Go の 3 ランタイムだけ先に揃え、C# / C++ / WASM は follow-up PR を issue 化

関連ドキュメント

© ayutaz, MIT. 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/check-runtime-parity of ayutaz/piper-plus.

Open the folder on GitHubat commit 9c6946e

Compare with similar skills

Check Runtime Parity next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Check Runtime Parity compared with similar skills
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Check Runtime Parity this skillayutaz/piper-plus220—~972Automated safety check: PassMIT
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Fory Performance Optimizationapache/fory4.6k—~2.2kAutomated safety check: PassApache-2.0
Onnxtxtonnx/onnx22k—~1.3kAutomated safety check: PassApache-2.0
Quark Installamd/Quark181—~1.8kAutomated safety check: NotesMIT
Genie API Service Docsqualcomm/qai-appbuilder246—~840Automated safety check: PassCustom licence

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Works with

Categories

Questions about Check Runtime Parity

What does Check Runtime Parity do?

推論パスの canonical Python (exportonnx.py / vits/models.py:VitsModel.infer) を変更した PR で、6 ランタイム (Python runtime / Rust / Go / C / C++ / WASM) の inference path が追随しているかを git diff で確認。PR. Check Runtime Parity is an agent skill from ayutaz/piper-plus.

When should I use Check Runtime Parity?

Check Runtime Parity fits situations like: AI & LLM Engineering work in your project.

How do I install Check Runtime Parity in Claude Code?

Run `npx skills add ayutaz/piper-plus --skill check-runtime-parity -a claude-code`. Or copy the skill folder (.claude/skills/check-runtime-parity in ayutaz/piper-plus) into .claude/skills/check-runtime-parity in your project. Claude Code loads it when a task matches its description.

How do I install Check Runtime Parity in Codex?

Run `npx skills add ayutaz/piper-plus --skill check-runtime-parity -a codex`. Or copy the skill folder (.claude/skills/check-runtime-parity in ayutaz/piper-plus) into .agents/skills/check-runtime-parity in your project. Codex loads it when a task matches its description.

Can I use Check Runtime Parity 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 ayutaz/piper-plus --skill check-runtime-parity -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/check-runtime-parity, .gemini/skills/check-runtime-parity, .github/skills/check-runtime-parity and .opencode/skills/check-runtime-parity in your project.

What does Check Runtime Parity need to run?

Going by SKILL.md and its folder, Check Runtime Parity needs the command-line tools its instructions call (python and git). Our summary lists: Python 3; Docker. Its frontmatter pre-approves these tools: Bash(git diff *), Bash(git status *), Bash(grep *), Bash(ls *).

Does Check Runtime Parity access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Check Runtime Parity 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 Check Runtime Parity use?

Check Runtime Parity is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Check Runtime Parity use?

About 972 tokens (SKILL.md is roughly 3.9k 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 Check Runtime Parity?

Skills that share tags, products or a category with Check Runtime Parity: Test Runner (noumena-labs/Sipp, 121 stars), Fory Performance Optimization (apache/fory, 4.6k stars), Onnxtxt (onnx/onnx, 22k stars) and Quark Install (amd/Quark, 181 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Check Runtime Parity?

ayutaz (a GitHub user) maintains it in ayutaz/piper-plus, which has 220 GitHub stars. The repository holds 20 skills in this directory. The repository was last updated on October 8, 2026.

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