# Ai Config Compress

> >

- **Type:** Skill
- **Install:** `agentstack add skill-fabis94-universal-ai-config-ai-config-compress`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [fabis94](https://agentstack.voostack.com/s/fabis94)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [fabis94](https://github.com/fabis94)
- **Source:** https://github.com/fabis94/universal-ai-config/tree/main/src/seed-types/meta-instructions/templates/skills/ai-config-compress

## Install

```sh
agentstack add skill-fabis94-universal-ai-config-ai-config-compress
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Prompt Compress

Compress LLM instructions with calibrated risk. Research shows ~60% of instruction
tokens are removable without degrading output quality — and compression often
_improves_ performance by concentrating model attention.

## Core Principles

1. **Instructions are the most sensitive prompt component.** Apply maximum
   compression to examples and context, moderate compression to structure,
   minimal compression to core behavioral rules.
2. **Semantic equivalence ≠ behavioral equivalence.** Two phrasings that mean the
   same thing to a human can produce different model behavior. Every change
   beyond mechanical cleanup carries nonzero risk.
3. **Compress in tiers.** Apply safest changes first, present riskier changes as
   suggestions. The user decides how far to go.

## Workflow

### 1. Analyze

Before compressing anything, analyze the input:

- Count tokens (estimate: words × 1.3 for English)
- Identify sections by function: safety, formatting, tone, behavior, examples,
  context, metadata
- Detect duplicates: rules that express the same constraint in different words
- Flag filler: politeness markers, hedging, verbose connectives
- Note structural issues: scattered related rules, inconsistent formatting
- Identify examples and assess whether they're redundant with stated rules

Present a brief analysis summary with estimated savings per tier.

### 2. Compress in Tiers

Apply changes tier by tier. For each tier, show a diff and token savings.

#### Tier 1 — Mechanical (auto-apply, safe)

These changes preserve exact meaning. Apply all of them:

- Fix typos and inconsistent punctuation
- Normalize whitespace (double spaces, trailing spaces, excessive blank lines)
- Apply word-level substitutions from `references/substitutions.md`
- Remove pure filler: "please note that," "it is important to," "keep in mind"
- Remove politeness in system prompts: "please," "kindly," "if you don't mind"
- Strip unnecessary articles in imperative instructions ("Write the response" → "Write response")
- Remove self-referential meta-commentary ("The following rules govern your behavior:" → just list the rules)

#### Tier 2 — Structural (recommend, low-medium risk)

These reorganize without changing meaning, but removal of "redundant" rules
may remove useful reinforcement:

- **Deduplicate**: Merge rules expressing the same constraint. Keep the most
  specific version. Example: "Be professional" + "Maintain professional tone" +
  "Always respond professionally" → "Maintain professional tone."
- **Group by function**: Collect scattered rules under section headers.
  Consolidating related rules eliminates repetitive framing ("When responding...",
  "In your responses...", "Your responses should...").
- **Flatten conditionals**: Convert nested if-else to flat patterns.
  "If user asks X, check Y, and if Y then Z" → "X + Y → Z; X + ¬Y → W"
- **Remove hedging on directives**: "You should try to ensure responses are
  accurate" → "Be accurate." Only where the hedge adds no real nuance.
- **Trim verbose examples**: If 5+ examples illustrate the same pattern, keep
  2-3 that cover distinct cases. Flag which ones you'd cut and why.

#### Tier 3 — Semantic (suggest only, medium-high risk)

These change wording while attempting to preserve intent. Present as suggestions
with explicit risk notes. Never auto-apply:

- **Telegraphic style**: Drop articles, pronouns, connectives from behavioral
  rules. "When the user asks you a question, you should provide a clear and
  concise answer" → "Answer questions clearly and concisely."
- **Principalize examples**: Replace remaining examples with a principle
  statement. "Example: 'Hello!' → 'Hi there!' / 'Hey' → 'Hello!'" →
  "Mirror greeting energy, match formality level."
- **Merge overlapping rules**: Combine rules that address related behaviors.
  Only where the merged version clearly covers both originals.
- **Remove examples entirely**: If the rule is clear without them. Flag as
  HIGH RISK — few-shot examples reduce prompt sensitivity by ~30%.
- **Reorder sections**: Move most critical rules to beginning and end of prompt
  (attention U-curve). Flag as MEDIUM RISK — reordering alone can cause
  significant behavioral shifts.

#### Tier 4 — Aggressive (flag only, high risk)

Only mention these as possibilities. Never draft them without explicit user request:

- Removing safety/guardrail instructions
- Changing role/persona framing
- Switching prompting strategies (zero-shot ↔ chain-of-thought)
- SPR-style compression (reducing to associative priming cues)
- Removing entire sections deemed low-value

### 3. Present Results

Output a compression report with this structure:

```
## Compression Report

**Original**: ~{n} tokens ({word_count} words)

### Tier 1 — Mechanical [{savings}% reduction]
{compressed text with changes}

### Tier 2 — Structural [{cumulative savings}% reduction]
{compressed text with Tier 1+2 applied}
Changes made:
- {change 1}: {rationale}
- {change 2}: {rationale}

### Tier 3 — Suggestions [{potential additional savings}%]
- [ ] {suggestion 1} — saves ~{n} tokens — RISK: {level} — {why risky}
- [ ] {suggestion 2} — saves ~{n} tokens — RISK: {level} — {why risky}

### Tier 4 — Aggressive options [{potential savings}%]
- {option}: {what it would save} — {what might break}

**Summary**: Tier 1+2 achieves ~{n}% reduction ({old} → {new} tokens).
Tier 3 suggestions could reach ~{n}% total if accepted.
```

### 4. Deliver

- If the user wants a specific tier applied, produce the final compressed text
  with all changes through that tier
- If they want to cherry-pick Tier 3 suggestions, apply only selected ones
- Always provide the final compressed version as a clean, copy-pasteable block

## Important Caveats to Communicate

- Compression effectiveness depends on the target model. What works for Claude
  may not work identically for GPT or Gemini.
- Larger/newer models tolerate more compression. If targeting smaller models,
  be more conservative.
- The only real validation is testing compressed prompts against actual tasks.
  This skill optimizes for likely preservation, not guaranteed preservation.
- Examples are disproportionately valuable. Cutting examples saves the most tokens
  but carries the most risk.

## Reference Files

- Read `references/substitutions.md` for the mechanical substitution dictionary
  used in Tier 1. Load this before applying Tier 1 changes.
- Read `references/examples.md` for before/after compression examples across
  different instruction types. Consult when unsure about a compression decision.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [fabis94](https://github.com/fabis94)
- **Source:** [fabis94/universal-ai-config](https://github.com/fabis94/universal-ai-config)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-fabis94-universal-ai-config-ai-config-compress
- Seller: https://agentstack.voostack.com/s/fabis94
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.