context-compressor

name	context-compressor
description	Compress long conversation histories, large code files, research results, and documents by 70% without losing critical information. Triggers when context window fills up, when summarizing previous steps in multi-step tasks, before loading large files into context, or on 'summarize', 'compress', 'reduce context', 'save tokens'.

Context Compressor Protocol

Reduce content by 70% — preserve critical information, discard repetition and noise. Target: compressed output should be ~30% of original size with preserved information density.

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Workflow

1. Detect content type
2. Mark critical elements (these are never discarded)
3. Apply type-specific compression
4. Verify 70% target
5. Present compressed output

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Step 1: Detect Content Type

Content Type	Detection Criteria
Conversation history	User/assistant message pairs
Code file	Function/class structures, syntax
Research results	URLs, source references, data points
Meeting notes / document	Headers, bullet lists, decisions

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Step 2: Mark Critical Elements

These elements are never discarded:

• Decisions & conclusions: "We decided to use X", "Y was chosen"
• Errors & fixes: Discovered bugs, applied solutions
• Numerical data: Dates, version numbers, metrics, amounts
• Dependencies: "A must finish before B" constraints
• Action items: Who does what, when
• Current state: Latest version, current config, last decision

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Step 3: Type-Specific Compression

Conversation History

Discard: Greetings, acknowledgments ("OK", "Got it", "Thanks"), repeated explanations, intermediate reasoning that contradicts the final conclusion.

Keep: User's clear requests, decisions and rationale, error messages and fixes, current task status.

Format:

[CONVERSATION SUMMARY — N messages -> M lines]
Context: [what the task is, 1 sentence]
Decisions: [bullet list]
Current status: [where things stand]
Pending: [open questions if any]

Code File

Discard: Comments (except docstrings), excessive blank lines, long import lists (consolidate), temporary debug prints.

Keep: All function/class signatures (with parameters), return types and critical type annotations, exception handling, configuration constants. Summarize function bodies as single-line pseudocode.

Format:

# [COMPRESSED — original: ~N lines -> now: ~M lines]
class ClassName:
    """[Original docstring]"""
    def method_name(self, param: Type) -> ReturnType:
        # [What it does — 1 line summary]
        ...

Research / Web Results

Discard: Duplicate information, generic background, verbose URLs (use domain name), out-of-scope quotes.

Keep: Concrete data points, primary source findings, contradictory findings (keep both), directly relevant findings.

Format:

[RESEARCH SUMMARY — N sources -> M items]
Topic: [what was researched]

Key findings:
- [Finding 1] (Source: domain.com)
- [Finding 2] (Source: domain.com)

Contradictions:
- [Source A says X, Source B says Y]

Missing / unverified:
- [Information not found]

Documents / Meeting Notes

Discard: Intro/closing paragraphs (if no content), repetitive statements, generic commentary.

Keep: Decisions made, action items (who, what, when), alternatives discussed and why rejected, next steps.

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Step 4: Verify 70% Target

Original size   : ~N words / lines / tokens
Compressed size : ~M words / lines / tokens
Compression     : ~X% reduction

Target: 70% [Achieved / Below target]

If below target (< 50% reduction): re-scan for repetition, compress background more aggressively, convert long examples to single-line references.

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Output Header

Every compression starts with:

CONTEXT COMPRESSOR
Type    : [content type]
Before  : ~N [words/lines]
After   : ~M [words/lines]
Saving  : ~X% reduction

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When to Skip

• Content is already short (< 200 words / 50 lines)
• User said "all details matter"
• Legal, medical, or financial documents (information loss risk too high)

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Guardrails

• Never discard decisions or error resolutions — these are the most valuable context.
• Preserve contradictions — if two sources disagree, keep both.
• Show compression ratio — the user must see how much was removed.
• Cross-skill: works with memory-ledger (can compress ledger entries) and agent-reviewer (provides compressed history for retrospective).