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/vidyfoo/antigravity-skill-engine/session-managingnpx skills add VidyFoo/antigravity-skill-engine --skill session-managinggit clone --depth 1 https://github.com/VidyFoo/antigravity-skill-engineWrote 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/vidyfoo/antigravity-skill-engine/session-managing)<a href="https://agentmods.dev/skills/vidyfoo/antigravity-skill-engine/session-managing"><img src="https://agentmods.dev/badge/skills/vidyfoo/antigravity-skill-engine/session-managing.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 | $0.00031 | $0.01174 |
| Opus 5 | $0.00015 | $0.00587 |
| Sonnet 5 | $0.00006 | $0.00235 |
| Haiku 4.5 | $0.00003 | $0.00117 |
Grade A, and why
session-managing 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 4d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Session Lifecycle Skill
核心理念: 会话生命周期 = 继承 → 工作 → 沉淀 → 闭环升级
自动触发协议
本 Skill 无需手动调用，AI 会在适当时机自动激活。
INHERIT 模式 (继承)
触发时机:
- 新对话开始 (对话历史为空)
- 用户发出首个任务型请求
- 用户明确说 "开始"、"入职"、"onboarding"
执行动作:
1. view_file doc/memory.md (读取 TL;DR 10 条核心认知)
2. view_file .agent/rules/rule-one.md
3. (复杂任务) view_file doc/explanation/architecture.md
4. 静默继承，不打断用户工作流
输出: 无 (静默执行) 或简短确认
DEPOSIT 模式 (沉淀)
触发时机 (满足任一条):
- 产生了新的设计决策或架构选择
- 发现并解决了隐藏的技术问题
- 形成了可复用的编程模式或最佳实践
- 产生了"这个想法太妙了"的闪光洞察
- 对话即将结束且有沉淀价值
执行动作:
1. 提炼本次对话精华 (CORE/PATTERN/DECISION/TIP)
2. view_file doc/memory.md
3. 融合更新 doc/memory.md (去重升维)
4. 如为技术规范 → 写入 doc/specs/
5. 通知用户完成
输出:
## 📦 沉淀完成
**本次新增**:
- `[PATTERN]` {模式名}
- `[DECISION]` {决策名}
✅ memory.md 已更新 (累计 N 条)
手动覆盖
用户可随时说:
- "跳过 onboarding" → 禁用 INHERIT
- "沉淀本次对话" → 强制 DEPOSIT
- "不需要沉淀" → 禁用 DEPOSIT
Doc 系统集成 (Diátaxis)
INHERIT 读取链 (优先级从高到低)
| 层级 | 文件 | 策略 | 说明 |
|---|---|---|---|
| L1 必读 | doc/memory.md (TL;DR) |
始终 | 核心认知与原则 |
| L1 必读 | .agent/rules/rule-one.md |
始终 | 项目宪章与禁忌 |
| L2 按需 | doc/explanation/README.md |
架构与全貌 | 系统索引 |
| L3 上下文 | doc/explanation/subsystems/{xxx}.md |
特定 Feature | 深度理解子系统 |
| L3 上下文 | doc/specs/{xxx}.md |
特定 Feature | 查阅具体规范 |
| L4 入门 | doc/tutorials/quick-start.md |
新手/重置 | 环境搭建与启动 |
DEPOSIT 写入链
根据知识类型选择写入位置：
| 知识类型 (Diátaxis) | 写入目标 | 示例 |
|---|---|---|
| Core Wisdom (智慧/决策) | doc/memory.md |
设计哲学、架构决策、踩坑教训 |
| Understanding (原理/架构) | doc/explanation/subsystems/ |
模块架构图、核心机制解析 |
| Reference (规范/规则) | doc/specs/ |
API 定义、业务规则、边界情况 |
| How-To (指南/测试) | doc/how-to/ |
测试策略、调试指南、操作手册 |
| Archive (历史/草稿) | doc/archive/ |
过时的设计文档、被取代的方案 |
记忆格式规范
类型标签
| 标签 | 含义 |
|---|---|
[CORE] |
核心原则，永恒不变 |
[PATTERN] |
可复用模式 |
[DECISION] |
时间点决策 |
[TIP] |
实用技巧 |
What ships with it
1 file 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.
- 4d ago First seen · 138 lines · 31 tokens per session scan A 8e6039ab37b1
session-managing is a skill published in the GitHub repository VidyFoo/antigravity-skill-engine (12 stars, last pushed 7mo ago), licensed MIT. It adds 31 tokens to every session and 1,174 once invoked, about $0.0002 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
workspace-mirror
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This skill should be used when the user wants to "create a skill", "add a skill to plugin", "write a new skill", "improve skill description", "organize skill content", or needs guidance on skill structure, progressive disclosure, or skill development best practices for Claude Code plugins.
SRT Enhancer
This skill should be used when the user asks to "enhance srt subtitles", "optimize srt with original script", "fix srt typos from markdown", "compare srt with origin.md", "improve subtitle accuracy", "add spaces to srt", "優化 srt 逐字稿", "比對原稿修正字幕", or needs to refine SRT subtitle files by comparing them with an original…
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This skill should be used when the user asks to "add reference cards to srt", "annotate srt with cards", "insert cards into subtitles", "generate srt with card annotations", "add 字卡 to srt", or needs to add contextual reference cards below subtitle text blocks in SRT files.
Audio to SRT Converter
This skill should be used when the user asks to "convert audio to srt", "generate subtitles from audio", "create srt from mp3/wav/m4a/flac", "transcribe audio to subtitles", or needs to generate SRT subtitle files from audio files (MP3, WAV, M4A, FLAC, etc.) with customizable character limits and timeline adjustments.