AG Kit is a toolkit that gives Google Antigravity coding agents a structured workspace with rules, skills, specialist agents, workflows, persistent memory, orchestration, MCP guidance, and a safety hook. It is for building and operating agent workflows in Antigravity, and its catalogue entries provide parts of that workspace contract.
Borrowing it
Nothing to install: this file belongs to vudovn/ag-kit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vudovn/ag-kit/main/.agents/skills/context-compression/SKILL.mdgit clone --depth 1 https://github.com/vudovn/ag-kitWrote 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/vudovn/ag-kit/context-compression)<a href="https://agentmods.dev/skills/vudovn/ag-kit/context-compression"><img src="https://agentmods.dev/badge/skills/vudovn/ag-kit/context-compression/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/vudovn/ag-kit/context-compression"><img src="https://agentmods.dev/badge/skills/vudovn/ag-kit/context-compression.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.01185 |
| Opus 5.5 | $0.00015 | $0.00474 |
| Sonnet 5.5 | $0.00008 | $0.00237 |
| Haiku 4.5 | $0.00004 | $0.00119 |
Grade A, and why
context-compression 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 1mo 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Compression — Long Session Management
Keep sessions productive by compressing completed work while preserving key decisions.
Overview
Long sessions (30+ turns) cause context degradation — the AI loses track of earlier work, repeats itself, or forgets decisions. Context compression proactively summarizes completed phases so the context window stays focused on active work.
Token Impact: Recovers 5,000-15,000 tokens in long sessions by replacing verbose tool outputs with semantic summaries.
When to Compress
| Signal | Action |
|---|---|
| Session has 20+ turns | Consider proactive compression |
| Agent repeats earlier suggestions | Context is saturated — compress now |
| User says "we already discussed this" | Compress immediately |
| Switching to a new phase of work | Compress the completed phase |
| Large tool output (500+ lines) | Micro-compact the output |
Compression Levels
Level 1: Micro-Compact (Tool Output)
Compress individual tool outputs while retaining semantic content:
❌ Before (raw grep output — 200 lines, ~4,000 tokens):
src/auth/jwt.ts:15: import { verify } from 'jsonwebtoken'
src/auth/jwt.ts:23: export function validateToken(token: string) {
src/auth/jwt.ts:24: try {
src/auth/jwt.ts:25: const decoded = verify(token, SECRET)
... (195 more lines)
✅ After (micro-compact — 5 lines, ~100 tokens):
Grep results for "jwt": Found 8 files, 42 matches.
Key files: src/auth/jwt.ts (main JWT logic), src/middleware/auth.ts (middleware),
src/api/login.ts (token creation). Token validation at jwt.ts:23-40.
Error handling at jwt.ts:42-55. Secret loaded from env at jwt.ts:8.
Level 2: Phase Summary
Replace a completed work phase with a summary:
❌ Before (full research transcript — ~3,000 tokens):
[turn 1] Read package.json...
[turn 2] Read src/index.ts...
[turn 3] Grep for "auth"...
[turn 4] Found 8 files related to auth...
[turn 5] Read src/auth/jwt.ts...
... (10 more turns of exploration)
✅ After (phase summary — ~200 tokens):
## Research Phase Complete
- Project: Next.js 15 app with JWT auth
- Auth files: 8 files in src/auth/, src/middleware/, src/api/
- Token flow: login → create JWT → store in httpOnly cookie → validate in middleware
- Bug location: src/auth/jwt.ts:45 — expiry check uses `<` instead of `<=`
- Decision: Fix the comparison operator, add edge case test
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.
- 1mo ago First seen · 147 lines · 38 tokens per session scan A 1165b2a41922
context-compression is a skill published in the GitHub repository vudovn/ag-kit (8,199 stars, last pushed 3d ago), licensed MIT. It adds 38 tokens to every session and 1,185 once invoked, about $0.0002 per session on Opus 5.5. 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.
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