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 agentmods add skills/agiledon/openmole/openmole-applynpx skills add agiledon/openmole --skill openmole-applygit clone --depth 1 https://github.com/agiledon/openmoleWrote 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/agiledon/openmole/openmole-apply)<a href="https://agentmods.dev/skills/agiledon/openmole/openmole-apply"><img src="https://agentmods.dev/badge/skills/agiledon/openmole/openmole-apply.svg" alt="Measured on agentmods" 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 | $0.00024 | $0.00650 |
| Opus 5 | $0.00012 | $0.00325 |
| Sonnet 5 | $0.00005 | $0.00130 |
| Haiku 4.5 | $0.00002 | $0.00065 |
Grade A, and why
openmole-apply 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 5d 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.
What it actually says
OpenMole Apply — 执行重构
路径解析说明：
{cwd}= 用户运行 mole 命令时的当前工作目录{config_dir}= OpenMole 用户级配置目录（~/.config/openmole/）
何时使用
用户运行 mole:apply，且当前 change 有未完成任务。
工作区解析
- 读取
{cwd}/openmole/config.yaml→current_change {change_dir}={cwd}/openmole/changes/{change_name}/- 无
current_change→ 停止，提示先mole:explore
选取任务
- 读取
{change_dir}/tasks.md§3 - 下一
[ ]的 B-Txx，依赖均已[x] - 每次 invocation 仅一个任务
执行步骤
| 步骤 | 动作 |
|---|---|
| ① | 确认 BS-ID 与 badsmells 一致 |
| ② | 补测（pytest/JUnit 等） |
| ③ | 测绿 |
| ④ | 重构（[SDD] 须先确认 SDD 已修订） |
| ⑤ | 回归测绿 |
| ⑥ | 用户确认 |
完成后
{change_dir}/tasks.md标记[x]- 满足 DoD 时更新
{change_dir}/badsmells.md§2.0
OpenMole 规约摘要（内嵌于各 Skill，非独立文件）
constitution §3 — 八项第一性原则
清晰性、一致性、可读性、复用性、可扩展性、健壮性、安全性、简洁性。
constitution §4 — 标准重构步骤
- 确认坏味道条目
- 确定测试覆盖；无覆盖则先写测试
- 运行测试 → 全绿
- 执行重构（最小 diff，对准 BS-ID）
- 回归测试 → 全绿
- 用户确认（未确认不得标记完成）
constitution §5 — 执行粒度
每次 mole:apply 仅处理一个未完成任务。
specification §4 — badsmells 条目
七字段 + §2.0 状态：未清除 / 已消除 / 部分残余。
specification §7 — 修订历史
升版时 提交版本 = git rev-parse HEAD，未提交填 —。
RED FLAGS
- 测试未绿仍标记完成
- 跳过用户确认
- 一次 apply 多个任务
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.
- 5d ago First seen · 76 lines · 24 tokens per session scan A db30ca6f5f5d
openmole-apply is a skill published in the GitHub repository agiledon/openmole (23 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 650 once invoked, about $0.0001 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…