Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add HorizonRobotics/OE-Skills --skill hmctgit clone --depth 1 https://github.com/HorizonRobotics/OE-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/horizonrobotics/oe-skills/hmct)<a href="https://agentmods.dev/skills/horizonrobotics/oe-skills/hmct"><img src="https://agentmods.dev/badge/skills/horizonrobotics/oe-skills/hmct/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/horizonrobotics/oe-skills/hmct"><img src="https://agentmods.dev/badge/skills/horizonrobotics/oe-skills/hmct.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00208 | $0.03945 |
| Opus 5 | $0.00104 | $0.01972 |
| Sonnet 5 | $0.00042 | $0.00789 |
| Haiku 4.5 | $0.00021 | $0.00394 |
Grade A, and why
hmct-workflow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
HMCT 工作流路由
本 Skill 是 HMCT 工具链的统一入口，根据用户意图自动分发到对应子流程。
路由规则
用户请求
│
├─ 意图：模型转换 / 量化构建 / PTQ，且提供了校准数据（cali_data_dir）
│ └─→ 路由 A：完整量化构建
│
├─ 意图：模型转换 / 验证模型 / 快速检查，未提供校准数据
│ └─→ 路由 B：快速验证
│
├─ 意图：精度调优 / cosine similarity 不达标 / 混精度配置
│ └─→ 路由 C：精度调优工作流
│
├─ 意图：单项 debug 分析（灵敏度、分布、累积误差等）
│ └─→ 路由 D：精度 Debug 工具
│
└─ 不确定
└─→ 询问用户意图后再路由
路由 A：完整量化构建（build）
触发条件： 用户希望执行模型量化转换，且提供了校准数据。
关键词： 模型转换、量化构建、build_model、PTQ 构建、校准
需要收集的参数
必填
| 参数 | 说明 |
|---|---|
--onnx_path |
输入 ONNX 模型路径 |
校准数据（二选一）
| 参数 | 说明 |
|---|---|
--cali_data_dir |
校准数据目录（子目录名需与模型输入名一致） |
--cali_dict_path |
cali_dict JSON 文件路径，指定后将覆盖 --cali_data_dir |
可选参数
| 参数 | 默认值 | 说明 |
|---|---|---|
--march |
nash-p |
BPU 芯片架构 |
--input_dict_path |
- | input_dict JSON（input_shape / transformer / color_convert 等） |
--quant_config_path |
- | quant_config JSON（PTQ 量化配置） |
--name_prefix |
model |
输出模型名称或路径前缀 |
--quiet |
关闭 | 关闭 verbose 输出 |
执行方式
# 最简：ONNX + 校准目录
python3 HMCT_Skill/reference/run_build.py build \
--onnx_path <模型路径> \
--cali_data_dir <校准数据目录> \
--march <芯片架构> \
--name_prefix <输出前缀>
# 自定义 cali_dict + input_dict
python3 HMCT_Skill/reference/run_build.py build \
--onnx_path model.onnx \
--cali_dict_path cali_dict.json \
--input_dict_path input_dict.json \
--march nash-e
# 指定 quant_config
python3 HMCT_Skill/reference/run_build.py build \
--onnx_path model.onnx \
--cali_data_dir ./cali_data \
--quant_config_path quant_config.json
执行步骤
- 确认
--onnx_path，未提供则询问 - 确认校准数据来源：
--cali_data_dir或--cali_dict_path，未提供则询问 - 根据用户需求收集可选参数（
--input_dict_path/--quant_config_path/--name_prefix） - 确认
--march参数，未指定则使用默认值nash-p - 运行
run_build.py build命令 - 检查输出日志，确认构建成功
- 向用户报告结果和输出文件路径
参考文档
详细参数说明见 reference/build_model.md
路由 B：快速验证（check）
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- j6-hmct-cosine-similarity-tuning/script/get_sensitivity_of_nodes.py 1.5 KB runs code
- j6-hmct-cosine-similarity-tuning/script/hmct_precision_tuning.py 39 KB runs code
- j6-hmct-cosine-similarity-tuning/SKILL.md 13 KB
- reference/build_model.md 6.6 KB
- reference/debug_tools.md 16 KB
- reference/run_build.py 7.6 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 318 lines · 208 tokens per session scan A 30412db4ed55
hmct-workflow is a skill published in the GitHub repository HorizonRobotics/OE-Skills (19 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 208 tokens to every session and 3,945 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
spark-optimization
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
notebooklm
Install, authenticate, troubleshoot, and operate Gemini Notebook through the notebooklm-py CLI or typed async Python API. Use for notebook and source management, grounded chat and research, and artifact generation or download when the user mentions Gemini Notebook, notebooklm-py, the notebooklm CLI, or its Python API.…
debug-inference
Debug why inference.local, direct external inference, or supervisor-only system inference is failing. Use when the user cannot reach a local model server, has provider base URL issues, sees inference verification failures, hits protocol mismatches, or needs to diagnose inference on local vs remote gateways. Trigger…
oh-my-posh
Install, configure, or troubleshoot Oh My Posh/ohmyposh: shell init, themes, segments, Nerd Font icons, and prompt setup on PowerShell, zsh, bash, or fish.
eagle3-triage
Triage a failed EAGLE3 pipeline run. Identifies which step failed (data synthesis, hidden state dump, training, or benchmark), diagnoses root cause from logs, and suggests fixes. Use when user reports an EAGLE3 pipeline failure or asks why a specific step failed. Also helps debug new model support issues.
spark-engineer
Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure…