skills/context-compressor/SKILL.md
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'.
npx skillsauth add fatih-developer/fth-skills context-compressorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Reduce content by 70% — preserve critical information, discard repetition and noise. Target: compressed output should be ~30% of original size with preserved information density.
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
| 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 |
These elements are never discarded:
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]
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]
...
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]
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.
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.
Every compression starts with:
CONTEXT COMPRESSOR
Type : [content type]
Before : ~N [words/lines]
After : ~M [words/lines]
Saving : ~X% reduction
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