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 sheng3233836/vibe-coding-standards --skill context-optimizationgit clone --depth 1 https://github.com/sheng3233836/vibe-coding-standardsWrote 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/sheng3233836/vibe-coding-standards/context-optimization)<a href="https://agentmods.dev/skills/sheng3233836/vibe-coding-standards/context-optimization"><img src="https://agentmods.dev/badge/skills/sheng3233836/vibe-coding-standards/context-optimization/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/sheng3233836/vibe-coding-standards/context-optimization"><img src="https://agentmods.dev/badge/skills/sheng3233836/vibe-coding-standards/context-optimization.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 | $0.00057 | $0.01560 |
| Opus 5.5 | $0.00023 | $0.00624 |
| Sonnet 5.5 | $0.00011 | $0.00312 |
| Haiku 4.5 | $0.00006 | $0.00156 |
Grade A, and why
context-optimization 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 1mo ago First seen · 180 lines · 57 tokens per session scan A 11cde65d1586
context-optimization is a skill published in the GitHub repository sheng3233836/vibe-coding-standards (1 stars, last pushed 6mo ago), with no licence file. It adds 57 tokens to every session and 1,560 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 2026-08-31.
Other skills, from other repositories
context-injection
Place trusted contextual information into prompts or agent state using explicit boundaries, provenance, and templates. Use when relevant context has already been selected and must be inserted safely; use context-retrieval to find it or context-optimization to choose and order it.
prompt-engineering
Use when advanced prompt engineering — chain-of-thought, few-shot, tree-of-thought, self-consistency, meta-prompting, system design, debugging, and optimization for production AI systems. Use when working with prompt engineering.
dify-workflow
Use when dify AI workflow platform — LLM apps, knowledge bases, agents, workflow orchestration, API deployment. Use when working with dify workflow.
bootstrap-llm-synthesis
Construct the LLM synthesis prompt from project surface scan + optional tree-sitter context + optional Q&A answers. Call the LLM. Parse and validate the response into 6-8 structured memory entries with clarity tags and source traceability. Used as Stage 3 of the /gaai:bootstrap pipeline.
context-repo-mcp
Search, retrieve, version, and persist prompts, documents, and collections through the hosted Context Repo MCP. Use this skill whenever the user mentions Context Repo, saved prompts, prompt templates, stored documents, collections, semantic search, hierarchical document navigation, cited answers over stored documents…
context-management
Token budgeting, placement effects, RAG patterns, prompt caching, compression, and multi-turn context strategies for LLM applications. Use when dealing with context windows, token budgets, retrieval-augmented generation, long context, context overflow, caching costs, observation masking, or compressing LLM inputs.