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 a275618631/codex-antigravity-collaboration --skill context-pack-buildergit clone --depth 1 https://github.com/a275618631/codex-antigravity-collaborationWrote 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/a275618631/codex-antigravity-collaboration/context-pack-builder)<a href="https://agentmods.dev/skills/a275618631/codex-antigravity-collaboration/context-pack-builder"><img src="https://agentmods.dev/badge/skills/a275618631/codex-antigravity-collaboration/context-pack-builder.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 5.1 | $0.00048 | $0.00345 |
| Opus 5 | $0.00024 | $0.00172 |
| Sonnet 5 | $0.00010 | $0.00069 |
| Haiku 4.5 | $0.00005 | $0.00034 |
Grade A, and why
context-pack-builder 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 8d 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
上下文包建立器
Trigger
使用者要求「整理上下文」、「交接給 Codex」、「做 context pack」、「把長對話濃縮」時啟用。
Input Schema
必填：
raw_context
選填：
target_agentproject_goalconstraints
Workflow
- 擷取使用者背景、專案背景與目前目標。
- 彙整已做決策、目前狀態與下一步。
- 保留限制、禁止事項與重要假設。
- 移除聊天雜訊、重複討論與暫時性內容。
- 列出開放問題與需要人工確認的事項。
Output Schema
輸出：
projectuser_profilegoalsdecisionsconstraintscurrent_statenext_stepsopen_questions
Validation
- 不新增來源沒有支持的假設。
- 必須保留明確限制與禁止事項。
- 下一個 Agent 應能不重讀全文就接手。
KPI
- 目標省時：90%。
- 目標正確率：90%。
- 成功指標：接手者可直接繼續工作，少量追問即可。
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.
- 8d ago First seen · 53 lines · 48 tokens per session scan A 97ecd00c02b0
context-pack-builder is a skill published in the GitHub repository a275618631/codex-antigravity-collaboration (8 stars, last pushed 8d ago), licensed MIT. It adds 48 tokens to every session and 345 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 2026-08-31.
Other skills, from other repositories
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
antigravity-skill-orchestrator
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
agent-memory-mcp
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
agent-memory
A hybrid memory system that provides persistent, searchable knowledge management for AI agents.
ai-agents-architect
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
accint-solve
Route a goal through acc's scored-memory loop via accact(runtime="solve"); deliberate any returned brainframe and submit via continue.