zipai-optimizer
Optimizes token usage in prompts to save resources. It helps make communication with AI more efficient and cheaper.
What it does
This agent helps the AI be more efficient when processing information. It removes unnecessary details, organizes what's important, and makes the AI's responses shorter and more direct.
When to use it
- ✓When you want the AI to respond faster and more concisely.
- ✓When dealing with many files or logs and the AI needs to focus on the essentials.
- ✓To optimize the AI's 'memory' usage in long conversations.
Example of what to ask your AI
“Use the optimizer to summarize this error log and extract only the crucial information.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It works by removing extra words, focusing on the most important parts of the information, and structuring data compactly. This allows the AI to process everything faster.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill zipai-optimizerOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/zipai-optimizer/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
ZipAI: Context & Token Optimizer
When to Use
Use this skill when the request needs context-window-aware triage, prompt caching optimizations, concise technical output, ambiguity handling, or selective reading of logs, source files, JSON/YAML payloads, VCS output, or MCP tool results.
Rules
Rule 1 — Adaptive Verbosity (No Filler)
- Fixes: technical only. ZERO filler (e.g., "Certainly", "I understand", "Here is", "Sure").
- Analysis: full reasoning allowed.
- Direct Ask: max 15 words in ultra-dense telegraphic style. Omit grammatical helper constructs.
- Long Sessions: never re-summarize past thread context.
- Reviews: use structured headers:
[ISSUE],[SUGGESTION],[NITPICK].
Rule 2 — Ambiguity-First Execution
- Ask exactly ONE question if 2+ interpretations exist. Never stack questions.
- Default to minimal intervention for minor changes.
- Scope ambiguous requests to narrowest boundary.
Rule 3 — Prompt Caching & Prefix Stability
- Static-First Ordering: Structure prompts to place invariant components (system instructions, core rules, static tool schemas) at the top of the prompt.
- Isolate Dynamic Context: Append dynamic and volatile elements (active conversation history, recently read file contents, CLI execution outputs) at the very end of the prompt to protect and reuse the cached prefix.
- Prefix Integrity: Avoid interleaving new queries or dynamic variables inside static system blocks. Keep the static instructions strictly invariant.
- Cached Files Reuse: Reuse already loaded file contents present in the conversation history; do not re-read files unless explicitly updated.
Rule 4 — Semantic Input Pruning & Log Compression
- Traceback Extraction: When handling error or build outputs, parse and filter logs using grep/regex to extract only tracebacks, error statements, and a maximum of 3-5 lines of context around them. Strip all info logs, successful build tasks, and redundant progress messages.
- Skeletal Code Viewing (AST): For large files (>300 lines), do not view the full file. Use
grep -nE "^(class|def|async def|function|const|let|var).*="(or language equivalents) to view class and function headers first, then target specific ranges withview_file. - Smart JSON/YAML Crusher: Minify structured inputs. Strip pretty-printing whitespaces, comments, and unused fields from JSON/YAML payloads before placing them in context. Convert large arrays to dense CSV or key-value listings if they are queried.
Rule 5 — Surgical & Compact Output
- Local Replacements: Perform edits using surgical tools (
str_replaceor single-hunk diffs). Never reprint unchanged surrounding code or perform full-file reprints. - Batch Modifies: Consolidate multiple non-contiguous edits in a single file into a single multi-replace chunk operation, ordered from leaf dependencies upward.
- Differential Output: Limit conversational responses to the exact modified blocks, avoiding conversational code repetition.
Rule 6 — Telegraphic Grammar & Density
- Syntax Compression: Strip articles ("a", "an", "the"), redundant helper verbs ("to be", "to have", "do"), and politeness/softening modifiers ("please", "simply", "just", "easy").
- Structure: Format output blocks into dense semantic mappings (
key: val), short bullet lists, and compact tables. Avoid paragraphs of text.
Rule 7 — Token-Budget Reasoning (CoT Optimization)
- Direct Mode: Skip long planning/thinking cycles for trivial, deterministic edits (typos, formatting, import adjustments).
- Abbreviated Thoughts: Keep thought blocks compact. Never reprint code snippets or copy-paste file blocks inside thoughts. Reference files via path and lines (e.g.
file.py#L12-18).
Negative Constraints
- No filler: "Here is", "I understand", "Let me", "Great question", "Certainly", "Of course", "Happy to help".
- No blind truncation of stacktraces or error logs.
- No full-file reads on large files.
- No re-reading files already in context.
- No multi-question clarification dumps.
- No silent bundling of unrelated changes.
- No full git diff ingestion on large changesets — extract hunks only.
- No git log beyond 20 entries unless a specific range is requested.
- No full MCP object inspection when field-level access suffices.
- No MCP mutations without prior read of current resource state.
- No SHA reuse across sessions for file updates.
Example
User request:
…
FAQ
Why does the AI sometimes talk too much?
The AI might include extra information to be clear or fill space. This agent teaches the AI to be more direct and avoid 'fluff'.
What does 'token' mean?
Tokens are like pieces of words or characters that the AI uses to understand and generate text. Fewer tokens mean faster and cheaper processing.
Can it help me write code?
Yes, it can help the AI analyze and optimize code more efficiently by focusing only on relevant parts.
Details
Original description by the author
Ultra-dense token optimizer skill for prompt caching, log pruning, AST-based inspection, and minified JSON payloads.
Popularity
- GitHub stars
- 48k
- Ranking position
- #4625
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
More skills from this repository
00-andruia-consultant
Helps your agent start or improve AI projects. It analyzes code, suggests tools, and creates action plans in Spanish.
sickn33/agentic-awesome-skills
007
Lets your agent perform security audits and threat modeling. It's useful for protecting projects against attacks and vulnerabilities.
sickn33/agentic-awesome-skills
10-andruia-skill-smith
Helps your agent create or improve AI tools. It defines how tools work, writes documentation, and validates the structure.
sickn33/agentic-awesome-skills
20-andruia-niche-intelligence
Lets your agent create a summary about a specific industry. It's useful for understanding the context, rules, usage patterns, and risks of a project, using reliable sources.
sickn33/agentic-awesome-skills
Similar skills
bengali-nlp
Has your agent process Bengali text. It helps understand and work with the language, normalizing text and handling its unique characteristics.
affaan-m/ECC
prompt-caching-strategy
Helps your agent optimize how it uses prompts for language models. It analyzes how prompts are made to find ways to cut costs and make responses faster by reusing information when possible.
affaan-m/ECC
prompt-optimizer
Has your agent analyze and improve the prompts you write. It suggests how to add information or change the wording so the AI model understands better and gives the desired answer.
affaan-m/ECC