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/joaoequer/oficina/token-efficiencynpx skills add JoaoEquer/Oficina --skill token-efficiencygit clone --depth 1 https://github.com/JoaoEquer/OficinaWhat 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 | $0.00105 | $0.01122 |
| Opus 5 | $0.00053 | $0.00561 |
| Sonnet 5 | $0.00021 | $0.00224 |
| Haiku 4.5 | $0.00011 | $0.00112 |
Grade C, and why
token-efficiency scanned grade C with 2 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 2d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://raw.githubusercontent.com/rtk-ai/rtk/refs/heads/master/install.sh | sh Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://raw.githubusercontent.com/rtk-ai/rtk/refs/heads/master/install.sh | sh How it starts
The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Token efficiency
Tokens are the working memory of the session. Every verbose command output gets re-read on every subsequent turn, so waste compounds: a 2,000-token git diff costs ~20,000 tokens over the next 10 turns. Two fronts: tooling (RTK) and habits.
RTK (Rust Token Killer)
rtk-ai/rtk is an open-source CLI proxy (single Rust binary, Apache 2.0) that compresses command output before it reaches the context window — 60–90% savings on test runners, git operations and package managers. It runs locally and hooks into the agent's official extension points (Claude Code PreToolUse hook); it does not touch API traffic.
Setup (once per machine)
# Install (official script; on Windows, native binary works since v0.37.2)
curl -fsSL https://raw.githubusercontent.com/rtk-ai/rtk/refs/heads/master/install.sh | sh
# Activate for your harness — hook-only avoids extra per-turn context
rtk init -g --hook-only # Claude Code
rtk init -g --gemini # Gemini CLI
rtk init -g --agent cursor # Cursor
Restart the agent afterwards. The hook rewrites Bash calls transparently (git status → rtk git status); the agent doesn't need to know RTK exists. Prefer --hook-only: the alternative injects an RTK.md into context on every turn, which spends tokens to save tokens.
What it actually covers (calibrate expectations)
- Big wins: test runners showing failures only (
pytest,vitest,cargo test— 90%+),git diff/log/status, package manager install noise,tsc/lint output grouped by file. - Not covered: the agent's native Read/Grep/Glob tools bypass the Bash hook. In read/edit-heavy sessions RTK has little to intercept — that's expected, not broken.
- Verify it's working:
rtk gainshows accumulated savings;rtk discoverfinds commands you ran that could have been compressed. - Failure safety: when a command fails, RTK saves the full unfiltered output to disk so nothing important is lost.
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.
- 2d ago First seen · 55 lines · 105 tokens per session scan C 62744532634b
token-efficiency is a skill published in the GitHub repository JoaoEquer/Oficina (2 stars, last pushed 1mo ago), licensed MIT. It adds 105 tokens to every session and 1,122 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
token-saver
Minimize token consumption & maximize prompt cache hit rate. Use when user asks to save tokens, reduce cost, improve cache hit rate, or be more concise.
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
common-llm-security
OWASP LLM Top 10 (2025) audit checklist for AI applications, agent tools, RAG pipelines, and prompt construction. Use when performing any security review touching LLM client code, prompt templates, agent tools, or vector stores.
prompt-engineering
Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic…
hermes-context-optimization
Use this when the user asks about Hermes prompt/context size, “hello” startup cost, compression behavior, memory/profile bloat, tool-schema overhead, skill loading, session-store/search-index storage, or multimodal/visual-context approaches such as Snapcompact.
llm
Guidelines for implementing LLM (Language Model) functionality in the application.