zipai-optimizer
Ultra-dense token optimizer skill for prompt caching, log pruning, AST-based inspection, and minified JSON payloads.
Best use case
zipai-optimizer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Ultra-dense token optimizer skill for prompt caching, log pruning, AST-based inspection, and minified JSON payloads.
Teams using zipai-optimizer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/zipai-optimizer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How zipai-optimizer Compares
| Feature / Agent | zipai-optimizer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Ultra-dense token optimizer skill for prompt caching, log pruning, AST-based inspection, and minified JSON payloads.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 with `view_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_replace` or 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.
---
## Limitations
- **Brainstorming:** disable during creative/open-ended design phases.
- **Grep Blindness:** key context may fall outside filter boundaries.
- **Overshadowing:** aggressive pruning may drop micro-variables in long sessions.Related Skills
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