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-checkpoint-previewgit 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-checkpoint-preview)<a href="https://agentmods.dev/skills/flanliulf/speclite/speclite-checkpoint-preview"><img src="https://agentmods.dev/badge/skills/flanliulf/speclite/speclite-checkpoint-preview/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-checkpoint-preview"><img src="https://agentmods.dev/badge/skills/flanliulf/speclite/speclite-checkpoint-preview.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 | $0.00077 | $0.01182 |
| Opus 5 | $0.00039 | $0.00591 |
| Sonnet 5 | $0.00015 | $0.00236 |
| Haiku 4.5 | $0.00008 | $0.00118 |
Grade A, and why
speclite-checkpoint-preview 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] Checkpoint Preview 是 Speclite 实施与交付推进工作流 Skill,用于在目标项目中按配置语言、输出语言和工作流步骤完成对应制品或交付动作。
源入口说明:LLM-assisted human-in-the-loop review. Make sense of a change, focus attention where it matters, test. Use when the user says "checkpoint", "human review", or "walk me through this change".
[核心能力]
- Speclite 激活解析:解析三层 customize(base→team→user)、workflow.persistent_facts 和 workflow.on_complete,并通过 speclite resolve config --project-root {project-root} 加载 merged runtime config。
- 源制品发现与上下文加载:按 workflow 规约读取项目制品、配置字段、历史上下文和必要数据文件,保持源流程的输入发现语义。
- 步骤化工作流执行:按 references/workflow-details.md 与拆分后的 reference/step 文件逐步执行,遵守顺序、HALT 条件、菜单等待和状态推进规则。
- 模板化输出生成:使用 assets 中的模板或示例骨架生成文档、报告、规格或交付产物,输出语言服从 document_output_language。
- 质量校验与交接:执行清单、报告、状态同步或 completion handoff,并在退出前解析和执行 workflow.on_complete。
- 迁移一致性约束:当前运行规约只依赖 Speclite runtime,不读取旧运行目录、旧配置文件或旧命令命名空间。
[约定]
裸路径相对于 {skill-root} 解析;{project-root} 是目标项目工作目录;{speclite-runtime-root} 是 {project-root}/_speclite;{skill-name} 是目录 basename。
[激活流程]
触发后先解析 workflow,执行 activation_steps_prepend,加载 persistent_facts,运行 speclite resolve config --project-root {project-root},按 communication_language 与用户沟通,并执行 activation_steps_append。配置文件缺失或关键字段为空时必须 HALT;config.toml.example 只说明字段结构,不作为 runtime fallback。
customization 必须通过 `speclite resolve customization --skill {skill-root} --project-root {project-root}` 读取 merged JSON;`workflow.on_complete` 使用 `speclite resolve customization --skill {skill-root} --project-root {project-root} --key workflow.on_complete` 解析。默认 activation 不手写 TOML merge,不使用 `--human` 作为 machine input。
[执行流程]
1. 先完整阅读 references/workflow-details.md;该文件是从源入口转换后的权威工作流规约。随后按需读取 references/workflow-details.md、references/generate-trail.md、references/steps/step-01-orientation.md、references/steps/step-02-walkthrough.md、references/steps/step-03-detail-pass.md、references/steps/step-04-testing.md 等 reference 文件。
2. 执行工作流前,确认 {skill-root}、{project-root}、{speclite-runtime-root}、{skill-name} 四个路径变量均已明确。
3. 若 workflow 指向 step 文件,必须一次只读取当前 step,完整执行后再进入下一步;遇到菜单或用户确认点时 HALT 等待。
4. 生成或更新产物时,按源 workflow 的模板、清单、状态字段和输出位置要求执行,不得因为迁移而改变核心需求。
5. 收尾前运行 checklist 或质量检查,解析 workflow.on_complete,并在输出文档末尾追加本 Skill 的生成标注。
[注意事项]
- 名称、目录与 YAML name 字段保持 kebab-case 一致:speclite-checkpoint-preview。
- references/workflow-details.md 和配套 reference 文件均为有效执行规约,不是背景资料。
- 如工作流需要模板,必须从 assets/ 中读取,不得临时省略模板结构。
- 如工作流需要项目配置,必须读取目标项目运行时配置。
- config.toml.example 仅作字段结构参考,不作为 runtime fallback。
- 当前运行规约不得依赖旧运行目录、旧 YAML 配置或旧命令命名空间。
- 输出文档末尾必须追加 *本文档由 speclite-checkpoint-preview Skill 自动生成* 标注。
[生成信息] 本 Skill 由 speclite-skill-creator 自动生成。
What ships with it
11 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.
- CHANGELOG.md 351 B
- config.toml.example 562 B
- customize.toml 1.6 KB
- references/generate-trail.md 1.9 KB
- references/steps/step-01-orientation.md 4.9 KB
- references/steps/step-02-walkthrough.md 4.1 KB
- references/steps/step-03-detail-pass.md 4.9 KB
- references/steps/step-04-testing.md 2.6 KB
- references/steps/step-05-wrapup.md 1.5 KB
- references/workflow-details.md 3.3 KB
- SKILL.en.md 3.9 KB
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 · 50 lines · 77 tokens per session scan A 653497251429
speclite-checkpoint-preview is a skill published in the GitHub repository flanliulf/SpecLite (4 stars, last pushed 2mo ago), licensed MIT. It adds 77 tokens to every session and 1,182 once invoked, about $0.0004 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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