context-slim

name	Context Slim
description	Context window optimizer — save 40-70% with 3 compression strategies
author	hanabi-jpn
version	1.0.0
tags	["context","optimization","compression","tokens","efficiency"]

    ╭──────────────────────────────────────────╮
    │                                          │
    │   🗜  C O N T E X T  S L I M  🗜        │
    │                                          │
    │   BEFORE:                                │
    │   ████████████████████████░░ 92% FULL!   │
    │                                          │
    │            ╔═══════╗                     │
    │     ──────▶║ SLIM  ║──────▶              │
    │            ╚═══════╝                     │
    │                                          │
    │   AFTER:                                 │
    │   ████████░░░░░░░░░░░░░░░░ 34% FREE!    │
    │                                          │
    │    "Save 40-70% of your context."        │
    ╰──────────────────────────────────────────╯

Context Slim

🗜 -70% Tokens 📊 Dashboard 🔍 Dedup ⚡ Auto-Compress v1.0.0

Intelligent context window optimizer. Compresses, prioritizes, and manages context to prevent overflow while preserving critical information. Save 40-70% of your context budget.

Author: hanabi-jpn | Version: 1.0.0 | License: MIT Tags: context optimization memory compression performance tokens

---

Overview

Context window bloat is the #1 pain point in the OpenClaw ecosystem. Memory skills, conversation history, and project files accumulate until your agent hits context_length_exceeded errors. Context Slim solves this with intelligent compression, priority-based loading, and automatic deduplication.

┌──────────────────────────────────────────┐
│          CONTEXT SLIM PIPELINE           │
│                                          │
│  ┌─────────┐  ┌──────────┐  ┌────────┐  │
│  │ MEASURE │─▶│ PRIORITZE│─▶│COMPRESS│  │
│  │ Tokens  │  │ Score    │  │ 3 modes│  │
│  └─────────┘  └──────────┘  └────────┘  │
│       │                          │       │
│       ▼                          ▼       │
│  ┌─────────┐  ┌──────────┐  ┌────────┐  │
│  │ DEDUP   │─▶│  BUDGET  │─▶│ REPORT │  │
│  │ Merge   │  │  Enforce │  │ Savings│  │
│  └─────────┘  └──────────┘  └────────┘  │
└──────────────────────────────────────────┘

System Prompt Instructions

You are an agent equipped with Context Slim. When the user requests context optimization or when you detect context is getting large, follow these instructions:

Context Analysis

When slim status or slim analyze is requested:

1. Estimate current token usage by category:

   • System prompt and skill instructions
   • Conversation history (messages so far)
   • Loaded memory/knowledge files
   • Active file contents
   • Tool results in context

2. Identify top consumers:

   • Which skills contribute most tokens?
   • How many conversation turns are in context?
   • Are there duplicate or near-duplicate content blocks?

3. Display status:

╔══════════════════════════════════════════╗
║          Context Slim Status             ║
╠══════════════════════════════════════════╣
║ Total Usage:  87,400 / 128,000 tokens   ║
║ Budget Used:  ████████████████░░░  68%   ║
╠══════════════════════════════════════════╣
║ Breakdown:                               ║
║  System/Skills:  12,300  (14%)  ████     ║
║  Conversation:   34,200  (39%)  ████████ ║
║  Memory/Files:   28,900  (33%)  ███████  ║
║  Tool Results:   12,000  (14%)  ████     ║
╠══════════════════════════════════════════╣
║ Top Consumers:                           ║
║  1. byterover context:  18,200 tokens    ║
║  2. conversation (42 turns): 34,200      ║
║  3. project README.md: 6,400             ║
║  4. self-learning data: 4,300            ║
║  5. skill-guardian threats: 3,200        ║
╠══════════════════════════════════════════╣
║ Duplicates Found: 3 (est. 4,200 tokens) ║
║ Stale Content: 5 items (est. 8,100)     ║
║                                          ║
║ ⚠️ Warning: 68% used. Optimize soon.    ║
╚══════════════════════════════════════════╝

Compression Strategies

semantic — Meaning-preserving compression:

• Summarize verbose content into dense representations
• Keep all facts, names, numbers, and key conclusions
• Remove filler words, redundant explanations, examples
• Best for: conversation history, documentation
• Typical savings: 50-70%

structural — Structure-preserving compression:

• Keep headers, function signatures, file paths, code structure
• Summarize body content within each section
• Preserve the skeleton, compress the flesh
• Best for: code files, structured documents
• Typical savings: 40-60%

priority — Relevance-based compression:

• Score each context chunk by relevance to current task
• Keep high-relevance content in full
• Compress medium-relevance content
• Drop low-relevance content entirely
• Best for: multi-skill agents with lots of background context
• Typical savings: 30-50%

Priority System

Each context chunk is scored (1-5):

• 5 (Critical): Current task instructions, active file being edited, most recent user message
• 4 (High): Last 3 conversation turns, active skill context, current project config
• 3 (Medium): Project memory, frequently used patterns, installed skill instructions
• 2 (Low): Older conversation history (>5 turns ago), inactive skill data, cached results
• 1 (Background): General knowledge, stale memory, unused tool results

User can override: slim priority set <item> <1-5>

Token Budgets

Set hard limits per category to prevent any single source from consuming too much:

{
  "system_skills": 3000,
  "conversation": 40000,
  "memory_files": 15000,
  "active_files": 20000,
  "tool_results": 10000,
  "buffer": 5000
}

When a category exceeds its budget:

1. Try compression (strategy based on content type)
2. If still over: truncate oldest/lowest-priority items
3. Log what was removed

Deduplication Engine

Detect and merge:

• Exact duplicates: Same content appearing in multiple places
• Near-duplicates: Content with >80% similarity (different wording, same information)
• Stale versions: Older version of content that's been updated

Algorithm:

1. Hash each content block (sentence-level)
2. Compare hashes for exact matches
3. For non-exact: compare key entity overlap
4. Merge duplicates into single canonical version
5. Replace all references to point to canonical

Automatic Optimization

Context Slim can run automatically:

• When context reaches 70% capacity: warn
• When context reaches 80% capacity: suggest optimization
• When context reaches 90% capacity: auto-compress lowest-priority content

Commands

slim status — Quick context usage overview

Output:

╔══════════════════════════════════════════╗
║          Context Slim Status             ║
╠══════════════════════════════════════════╣
║ Total Usage:  87,400 / 128,000 tokens   ║
║ Budget Used:  ████████████████░░░  68%   ║
╠══════════════════════════════════════════╣
║ Breakdown:                               ║
║  System/Skills:  12,300  (14%)  ████     ║
║  Conversation:   34,200  (39%)  ████████ ║
║  Memory/Files:   28,900  (33%)  ███████  ║
║  Tool Results:   12,000  (14%)  ████     ║
╠══════════════════════════════════════════╣
║ ⚠️ Warning: 68% used. Optimize soon.    ║
╚══════════════════════════════════════════╝

slim analyze — Detailed analysis with top consumers, duplicates, recommendations

Output:

╔═══════════════════════════════════════════════════════╗
║           Context Slim — Deep Analysis                ║
╠═══════════════════════════════════════════════════════╣
║  Total Usage: 87,400 / 128,000 tokens (68%)          ║
║                                                       ║
║  Top Consumers:                                       ║
║  ──────────────────────────────────────────────        ║
║  1. Conversation (42 turns):   34,200 tokens (39%)   ║
║  2. byterover context:         18,200 tokens (21%)   ║
║  3. project README.md:          6,400 tokens  (7%)   ║
║  4. self-learning data:         4,300 tokens  (5%)   ║
║  5. skill-guardian threats:     3,200 tokens  (4%)   ║
║                                                       ║
║  Duplicates Found: 3                                  ║
║  ──────────────────────────────────────────────        ║
║  • Project description appears in 3 skill contexts    ║
║    (est. 2,100 tokens recoverable)                    ║
║  • Auth config repeated in 2 memory files             ║
║    (est. 1,200 tokens recoverable)                    ║
║  • Package.json contents duplicated in tool results   ║
║    (est. 900 tokens recoverable)                      ║
║                                                       ║
║  Stale Content: 5 items (est. 8,100 tokens)          ║
║  ──────────────────────────────────────────────        ║
║  • 12 conversation turns older than 30 minutes        ║
║  • 3 tool results from completed tasks                ║
║                                                       ║
║  Recommendations:                                     ║
║  1. Run `slim dedup` to save ~4,200 tokens           ║
║  2. Compress conversation (save ~17,000 tokens)       ║
║  3. Set budget for byterover (currently unbounded)    ║
║  4. Total potential savings: ~29,300 tokens (34%)    ║
╚═══════════════════════════════════════════════════════╝

slim compress — Auto-compress (picks best strategy per content type):

• Conversation history -> semantic compression
• Code/structured docs -> structural compression
• Skill instructions -> priority compression

Output:

╔═══════════════════════════════════════════════════════╗
║           Context Slim — Auto-Compress                ║
╠═══════════════════════════════════════════════════════╣
║  Analyzing content types and selecting strategies...  ║
║                                                       ║
║  Conversation (42 turns) → semantic compression       ║
║    Before: 34,200 tokens                              ║
║    After:  12,400 tokens                              ║
║    Saved:  21,800 tokens (63.7%)  ✅                  ║
║                                                       ║
║  Code files → structural compression                  ║
║    Before: 8,200 tokens                               ║
║    After:  4,100 tokens                               ║
║    Saved:  4,100 tokens (50.0%)  ✅                   ║
║                                                       ║
║  Skill instructions → priority compression            ║
║    Before: 12,300 tokens                              ║
║    After:  8,900 tokens                               ║
║    Saved:  3,400 tokens (27.6%)  ✅                   ║
║                                                       ║
║  Tool results → semantic compression                  ║
║    Before: 12,000 tokens                              ║
║    After:  5,200 tokens                               ║
║    Saved:  6,800 tokens (56.7%)  ✅                   ║
║                                                       ║
║  ═══════════════════════════════════════════════════   ║
║  Total saved: 36,100 tokens (41.3%)                   ║
║  New usage: 51,300 / 128,000 tokens (40%)            ║
║  Status: ████████░░░░░░░░░░░░ 40% — HEALTHY          ║
╚═══════════════════════════════════════════════════════╝

slim compress --strategy <semantic|structural|priority> — Force strategy

Output:

╔═══════════════════════════════════════════════════════╗
║     Context Slim — Semantic Compression               ║
╠═══════════════════════════════════════════════════════╣
║  Strategy: semantic (forced)                          ║
║  Target: All compressible content blocks              ║
║                                                       ║
║  Processing 6 content blocks...                       ║
║  [1/6] Conversation history:  34,200 → 11,800  ✅    ║
║  [2/6] byterover context:    18,200 →  6,400  ✅    ║
║  [3/6] Tool results:          12,000 →  4,800  ✅    ║
║  [4/6] self-learning data:     4,300 →  2,100  ✅    ║
║  [5/6] README.md:              6,400 →  3,200  ✅    ║
║  [6/6] Skill instructions:   12,300 →  9,800  ✅    ║
║                                                       ║
║  Total saved: 39,000 tokens (44.6%)                   ║
║  Key entities preserved: 142/142 (100%)              ║
║  New usage: 48,400 / 128,000 tokens (38%)            ║
╚═══════════════════════════════════════════════════════╝

slim budget set <category> <tokens> — Set budget:

• Categories: system_skills, conversation, memory_files, active_files, tool_results

Output:

✅ Budget updated: conversation → 30,000 tokens

  Category         │ Old Budget │ New Budget │ Current Use
  ─────────────────┼────────────┼────────────┼───────────
  system_skills    │    3,000   │    3,000   │   2,800
  conversation     │   40,000   │   30,000   │  34,200 ⚠️
  memory_files     │   15,000   │   15,000   │  12,400
  active_files     │   20,000   │   20,000   │   8,200
  tool_results     │   10,000   │   10,000   │   6,800
  buffer           │    5,000   │    5,000   │     —

  ⚠️ conversation is currently over the new budget.
  Run `slim compress` to bring it within limits.

slim budget show — Show current budgets and usage

Output:

╔═══════════════════════════════════════════════════════╗
║          Context Slim — Budget Overview               ║
╠═══════════════════════════════════════════════════════╣
║  Category       │ Budget  │ Used    │ %    │ Status   ║
║  ───────────────┼─────────┼─────────┼──────┼──────── ║
║  system_skills  │  3,000  │  2,800  │ 93%  │ ⚠️      ║
║  conversation   │ 30,000  │ 12,400  │ 41%  │ ✅      ║
║  memory_files   │ 15,000  │ 12,400  │ 83%  │ ⚠️      ║
║  active_files   │ 20,000  │  8,200  │ 41%  │ ✅      ║
║  tool_results   │ 10,000  │  6,800  │ 68%  │ ✅      ║
║  buffer         │  5,000  │    —    │  —   │ ✅      ║
║  ───────────────┼─────────┼─────────┼──────┼──────── ║
║  Total          │ 83,000  │ 42,600  │ 51%  │ ✅      ║
║                                                       ║
║  Model max: 128,000 tokens                            ║
║  Unallocated: 45,000 tokens                          ║
╚═══════════════════════════════════════════════════════╝

slim dedup — Run deduplication:

• Find and merge duplicates
• Report savings

Output:

╔═══════════════════════════════════════════════════════╗
║           Context Slim — Deduplication                ║
╠═══════════════════════════════════════════════════════╣
║  Scanning content blocks for duplicates...            ║
║  Hashing 48 content blocks...                         ║
║                                                       ║
║  Exact Duplicates Found: 2                            ║
║  ──────────────────────────────────────────────        ║
║  • Project description (3 copies → 1 canonical)      ║
║    Saved: 2,100 tokens                                ║
║  • Package.json in tool results (2 copies → 1)       ║
║    Saved: 900 tokens                                  ║
║                                                       ║
║  Near-Duplicates Found: 1 (>80% similarity)          ║
║  ──────────────────────────────────────────────        ║
║  • Auth config in memory vs active file (87% match)  ║
║    Merged into canonical version                      ║
║    Saved: 1,200 tokens                                ║
║                                                       ║
║  Total deduplicated: 3 items                          ║
║  Total saved: 4,200 tokens                            ║
║  New usage: 83,200 / 128,000 tokens (65%)            ║
╚═══════════════════════════════════════════════════════╝

slim profile — Profile each installed skill's context footprint:

╔═══════════════════════════════════════════╗
║     Skill Context Profile                 ║
╠═══════════════════════════════════════════╣
║ Skill            │ Tokens │ Growth/Session║
║ byterover        │ 18,200 │ +2,400 ⚠️    ║
║ self-learning    │  4,300 │ +200         ║
║ capability-evlvr │  1,800 │ +50          ║
║ summarize-pro    │    900 │ 0            ║
║ skill-guardian   │  3,200 │ 0            ║
╠═══════════════════════════════════════════╣
║ ⚠️ byterover is growing unbounded        ║
║ Recommendation: Set budget to 10,000      ║
╚═══════════════════════════════════════════╝

slim optimize — Full optimization run (analyze + compress + dedup):

• One command does everything
• Report savings

Output:

╔═══════════════════════════════════════════════════════╗
║        Context Slim — Full Optimization               ║
╠═══════════════════════════════════════════════════════╣
║  Step 1: Analyze...                              ✅   ║
║    Found 3 duplicates, 5 stale items, 2 over-budget  ║
║                                                       ║
║  Step 2: Dedup...                                ✅   ║
║    Removed 3 duplicates (saved 4,200 tokens)         ║
║                                                       ║
║  Step 3: Compress...                             ✅   ║
║    Conversation: semantic  (saved 21,800 tokens)     ║
║    Code files: structural  (saved 4,100 tokens)      ║
║    Skills: priority        (saved 3,400 tokens)      ║
║    Tool results: semantic  (saved 6,800 tokens)      ║
║                                                       ║
║  ═══════════════════════════════════════════════════   ║
║  Before: 87,400 tokens (68%)                         ║
║  After:  47,100 tokens (37%)                         ║
║  Saved:  40,300 tokens (46.1%)                       ║
║                                                       ║
║  Status: ████████░░░░░░░░░░░░ 37% — HEALTHY          ║
║  Optimization logged to .context-slim/history.jsonl  ║
╚═══════════════════════════════════════════════════════╝

slim history — Show compression history and cumulative savings

Output:

╔═══════════════════════════════════════════════════════╗
║         Context Slim — Compression History            ║
╠═══════════════════════════════════════════════════════╣
║  Date        │ Action     │ Saved    │ Method         ║
║  ────────────┼────────────┼──────────┼──────────────  ║
║  Mar 01 14:30│ optimize   │ 40,300   │ auto-select    ║
║  Mar 01 12:15│ dedup      │  4,200   │ hash-compare   ║
║  Feb 28 16:45│ compress   │ 28,100   │ semantic       ║
║  Feb 28 10:20│ auto-90%   │ 12,400   │ priority       ║
║  Feb 27 14:00│ compress   │ 22,300   │ structural     ║
║  Feb 26 09:30│ dedup      │  3,800   │ hash-compare   ║
║                                                       ║
║  Cumulative Savings (7 days):                         ║
║  Total tokens saved:   111,100                        ║
║  Optimization runs:    6                              ║
║  Auto-triggers:        1 (90% threshold)              ║
║  Avg savings per run:  18,517 tokens                  ║
║                                                       ║
║  Data: .context-slim/history.jsonl                    ║
╚═══════════════════════════════════════════════════════╝

slim reset — Reset budgets and settings to defaults

Output:

╔═══════════════════════════════════════════════════════╗
║         Context Slim — Reset to Defaults              ║
╠═══════════════════════════════════════════════════════╣
║                                                       ║
║  ⚠️ This will reset all budgets and settings.         ║
║  Compression history will be preserved.               ║
║                                                       ║
║  Confirm reset? (yes/no): yes                         ║
║                                                       ║
║  Budgets reset:                                       ║
║    system_skills:  3,000  (was 3,000 — unchanged)    ║
║    conversation:  40,000  (was 30,000 — restored)    ║
║    memory_files:  15,000  (was 15,000 — unchanged)   ║
║    active_files:  20,000  (was 20,000 — unchanged)   ║
║    tool_results:  10,000  (was 10,000 — unchanged)   ║
║    buffer:         5,000  (was 5,000 — unchanged)    ║
║                                                       ║
║  Custom priority overrides: cleared (3 removed)       ║
║  Skill profiles: preserved                            ║
║  Hash cache: cleared (will rebuild on next dedup)     ║
║                                                       ║
║  ✅ Settings reset to defaults.                        ║
╚═══════════════════════════════════════════════════════╝

Data Storage

.context-slim/
├── config.json          # Budgets and settings
├── history.jsonl        # Compression events
├── profiles/
│   └── {skill}.json     # Skill context profiles
└── cache/
    └── hashes.json      # Content hashes for dedup

Error Handling

Context Slim handles compression and token management failures to prevent data loss and ensure context integrity.

Compression Failure

Scenario	Handling
Semantic compression produces empty output	Fall back to structural compression. If that also fails, keep original content unchanged and log the failure. Never replace content with an empty or corrupted summary.
Compressed output is LARGER than original	Discard the compressed version and keep the original. This can happen with very short content blocks where the summary overhead exceeds savings. Log the event for optimization.
Compression loses critical entities (names, numbers, dates)	Validate compressed output against a key-entity checklist extracted from the original. If any critical entity is missing, re-run compression with explicit entity preservation instructions.
Multiple compression strategies all fail	Mark the content block as "incompressible" and exclude it from future auto-compression. Apply budget enforcement by compressing other blocks instead.

Token Counting Errors

Scenario	Handling
Token count estimate diverges from actual	Context Slim uses heuristic estimation (approximately 1 token per 4 characters for English). Actual tokenizer counts vary by model. Build in a 10% safety margin on all budget calculations.
Model-specific tokenizer unavailable	Fall back to the character-based heuristic. Display a warning that token counts are approximate. Never block operations due to missing tokenizer.
Negative token count calculated	Floor all values at 0. Log the anomaly. This typically indicates a bug in the deduplication accounting — run slim analyze to reconcile.
Token count overflow on very large contexts	Cap tracking at the model's maximum context window size. Any content beyond the cap is flagged for immediate compression or removal.

Budget Overflow

Scenario	Handling
Category exceeds budget after new content added	Trigger automatic compression for that category (strategy selected by content type). If still over budget after compression, truncate oldest/lowest-priority items. Log every truncation with the removed content hash for recovery.
Multiple categories over budget simultaneously	Process in priority order: tool_results first (most ephemeral), then conversation, then memory_files, then active_files, then system_skills (most critical, compressed last).
Total context exceeds model maximum	Emergency mode: compress ALL categories to minimum viable content. Keep only: current user message, current task instructions, and most recent 2 conversation turns. Display warning with savings report.
Budget configuration invalid (negative values, exceeds model max)	Reject the configuration. Display current valid budgets. Suggest slim reset to restore defaults.

Deduplication Errors

Scenario	Handling
False positive: non-duplicate flagged as duplicate	Deduplication uses >80% similarity threshold. If merged content loses distinct information, undo the merge. Users can exclude specific blocks with slim priority set <item> 5 (critical items are never deduped).
Hash collision	Use content-length as secondary check. If two blocks have the same hash but different lengths, treat them as distinct.
Cache file corrupted	Rebuild hash cache from scratch by re-scanning all context blocks. This is slower but ensures correctness. Store backup in .context-slim/cache/hashes.json.bak.

Recovery

• All compression events are logged to .context-slim/history.jsonl with before/after token counts and content hashes.
• Use slim history to review what was compressed and when.
• Original content can be reconstructed from disk files — Context Slim never modifies source files.

Context Slim vs Manual Context Management

Feature	Context Slim	Manual Summarization	No Management (Default)	Simple Truncation
Token Savings	40-70% through intelligent compression	30-50% (depends on skill)	0% — context fills until error	20-40% but loses recent context
Information Preservation	Priority-scored: critical content kept in full	Varies — human judgment	N/A — everything kept until overflow	Poor — oldest content dropped blindly
Effort Required	Zero — automatic or one-command (slim optimize)	High — manual rewriting each session	Zero but hits context_length_exceeded errors	Low but lossy
Deduplication	Automatic — exact and near-duplicate detection	Manual — easy to miss duplicates	None — duplicates accumulate	None
Per-Skill Profiling	Tracks each skill's token footprint and growth rate	Not practical manually	No visibility	No visibility
Budget Enforcement	Hard limits per category with auto-compression	No enforcement — relies on discipline	No limits — first-come-first-served	Global limit only
Compression Strategies	3 specialized strategies (semantic, structural, priority)	One approach (manual rewriting)	None	One approach (drop oldest)
Recovery	Full history log, original files untouched	Lossy — original phrasing lost	N/A	Lossy — truncated content gone
Cost	Free (included with skill)	Engineer time per session	Free but causes errors and degraded performance	Free but loses information
Multi-Skill Awareness	Profiles each skill, identifies unbounded growth	No cross-skill visibility	No visibility	No visibility
Automatic Triggers	70% warn, 80% suggest, 90% auto-compress	None — must remember to summarize	None — crashes at 100%	None — must set up manually

FAQ

Q: Does Context Slim modify my actual files? A: No. It only manages what's loaded into the agent's context window. Your files on disk remain unchanged.

Q: How does it know what's in my context? A: It estimates based on loaded skills, conversation length, and active files. Exact token counts may vary by model.

Q: Will it remove important information? A: The priority system ensures critical content (current task, recent messages) is never removed. Only low-priority and duplicate content is compressed or dropped.

Q: Which compression strategy should I use? A: Use slim compress (automatic) and let Context Slim choose per content type. If you want manual control: use semantic for conversation history and documentation, structural for code files, and priority when you have many skills loaded but only need a few for the current task.

Q: How accurate are the token count estimates? A: Context Slim uses a heuristic of approximately 1 token per 4 characters (English text). This is within 10-15% of actual tokenizer counts for most models. A 10% safety margin is built into all budget calculations. For precise counts, the estimates are sufficient for budget management.

Q: Can I undo a compression? A: Compression only affects the in-memory context, not files on disk. If you need the full original content re-loaded, simply reference the source file again. All compression events are logged in .context-slim/history.jsonl for audit purposes.

Q: Does it work with all LLM models? A: Yes. Context Slim is model-agnostic. It works with any model supported by OpenClaw (Claude, GPT, Gemini, etc.). Token estimates may vary slightly between models, but the 10% safety margin accounts for this difference.

Q: What is the deduplication similarity threshold? A: Near-duplicate detection uses an 80% similarity threshold. Content blocks that share more than 80% of their key entities and structure are flagged as duplicates and merged into a single canonical version. You can protect specific blocks from deduplication by setting their priority to 5 (critical).

Q: How does Context Slim interact with other skills? A: Context Slim monitors all loaded skills' token footprints via slim profile. It can identify which skills consume the most context and which have unbounded growth. It does not modify other skills' behavior — it only manages how their output is stored in context.

Q: What happens during the automatic 90% trigger? A: When context usage reaches 90%, Context Slim automatically compresses the lowest-priority content blocks. It starts with priority 1 (background) content, then priority 2 (low) if needed. It never auto-compresses priority 4-5 content. A notification is displayed showing what was compressed and how many tokens were saved.