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 flanliulf/SpecLite --skill speclite-code-review-03-fixergit clone --depth 1 https://github.com/flanliulf/SpecLiteWrote 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/flanliulf/speclite/speclite-code-review-03-fixer)<a href="https://agentmods.dev/skills/flanliulf/speclite/speclite-code-review-03-fixer"><img src="https://agentmods.dev/badge/skills/flanliulf/speclite/speclite-code-review-03-fixer/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/flanliulf/speclite/speclite-code-review-03-fixer"><img src="https://agentmods.dev/badge/skills/flanliulf/speclite/speclite-code-review-03-fixer.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 | $0.00068 | $0.01031 |
| Opus 5 | $0.00034 | $0.00515 |
| Sonnet 5 | $0.00014 | $0.00206 |
| Haiku 4.5 | $0.00007 | $0.00103 |
Grade A, and why
speclite-code-review-03-fixer 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 7d 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
[技能说明] 根据代码审查结果评估的结论执行代码修复，并将修复执行总结追加到评估文档中。是跨 LLM 代码审查工作流中唯一允许修改源码的环节。
[核心能力] - 评估驱动修复：严格按照《代码审查结果评估文件》的结论执行修复，不自行扩大修复范围 - 自动定位评估文件：自动扫描并定位最新一轮的《代码审查结果评估文件》 - 精准定点修复：针对评估确认需要修复的问题逐一处理 - 修复记录追踪：将修复执行总结追加到评估文件的指定章节 - 修复验证：修复后验证代码编译/运行是否正常
[执行流程]
路径约定和文件名格式以 references/cr-config.md 为准。
Step 1：定位最新评估文件
- 接收用户指定的 Story 标识
- 读取 `references/cr-config.md` 获取路径约定
- 按配置中的 Story ID 规则提取 `{story-id}`
- 按配置中的代码审查目录格式确定路径
- 按配置中的审查评估文件名格式，扫描 code-review-dir 下匹配的文件
- 找到 round 值（n）最大的文件作为修复依据
- 读取该文件的完整内容，提取需要修复的问题列表
- 生成数据：evaluation-file-path、fix-items（待修复问题列表）
Step 2：制定修复计划
- 根据评估结论中"需要修复"的条目，制定修复计划
- 按优先级排序修复顺序
- 确认每个修复项的：
- 涉及的文件和代码位置
- 具体修复方案
- 预期效果
- 向用户展示修复计划供确认
- 生成数据：fix-plan（修复计划）
Step 3：逐项执行修复
- 按修复计划逐项执行代码修改
- 每项修复完成后记录：
- 修改了哪些文件的哪些位置
- 修改前后的关键差异
- 修复是否成功
- 生成数据：fix-results（修复执行结果列表）
Step 4：记录修复总结
- 将修复执行总结整理为结构化内容
- 将总结内容**追加**到最新一轮《代码审查结果评估文件》的 "## 修复执行记录" 章节中
- 如果该章节不存在，在文件末尾创建该章节
- 修复执行记录的开头必须包含元信息：
```
### 修复执行记录
- **Date**: <YYYY-MM-DD>
- **Model Used**: <当前执行本次修复的模型名称，如 Claude Opus 4、GPT-4o 等>
- **Fix Items**: <修复条目数>
```
- 完成后返回："✅ CR 修复执行完成，修复记录已追加到评估文件"
[注意事项]
- 只修复评估结论中明确标记为"需要修复"的问题，禁止自行扩大修复范围
- 禁止修改 Story 文档内容
- 修复总结追加到最新一轮（n 值最大）的《代码审查结果评估文件》中
- 路径约定和文件名格式以 references/cr-config.md 为准，不硬编码
- 始终使用中文输出修复记录
- 如果某项修复无法完成（如缺少上下文信息），标记为"待确认"并说明原因
- 修复后如有条件应运行相关测试验证修复效果
- 修复执行记录中的 Model Used 字段必须如实填写当前执行修复的模型名称，便于跨 LLM 追溯
- 如果找不到评估文件，立即停止并告知用户
What ships with it
2 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.
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.
- 7d ago First seen · 75 lines · 68 tokens per session scan A 0da89282971f
speclite-code-review-03-fixer is a skill published in the GitHub repository flanliulf/SpecLite (4 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 1,031 once invoked, about $0.0003 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 2026-09-03.
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