What it does. This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries that preserve decisions, files, risks, and next actions. Part of muratcankoylan/Agent-Skills-for-Context-Engineering (muratcankoylan/Agent-Skills-for-Context-Engineering).

Upstream	muratcankoylan/Agent-Skills-for-Context-Engineering
Skill file	skills/context-compression/SKILL.md
License	MIT
Author	Muratcan Koylan
Fetched	2026-09-10

Install

• npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill context-compression, or copy the skill folder into ~/.claude/skills/context-compression/.
• Raw file: curl -sL https://raw.githubusercontent.com/muratcankoylan/Agent-Skills-for-Context-Engineering/HEAD/skills/context-compression/SKILL.md

SKILL.md (verbatim)

name: context-compression
description: "This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries that preserve decisions, files, risks, and next actions."

Context Compression Strategies

When agent sessions generate millions of tokens of conversation history, compression becomes mandatory. The naive approach is aggressive compression to minimize tokens per request. The correct optimization target is tokens per task: total tokens consumed to complete a task, including re-fetching costs when compression loses critical information.

When to Activate

Activate this skill when:

• Agent sessions exceed context window limits
• Codebases exceed context windows (5M+ token systems)
• Designing conversation summarization strategies
• Debugging cases where agents "forget" what files they modified
• Building evaluation frameworks for compression quality
• Creating durable handoff summaries that preserve decisions, files, risks, and next actions

Do not activate this skill for adjacent work owned by other skills:

• General token-efficiency tactics such as masking, prefix caching, or partitioning: context-optimization.
• Diagnosing why a long context is failing before choosing a mitigation: context-degradation.
• Writing raw outputs, logs, or plans to files without summarizing them: filesystem-context.
• Designing long-term semantic memory across sessions: memory-systems.

Core Concepts

Context compression trades token savings against information loss. Select from three production-ready approaches based on session characteristics:

1. Anchored Iterative Summarization: Implement this for long-running sessions where file tracking matters. Maintain structured, persistent summaries with explicit sections for session intent, file modifications, decisions, and next steps. When compression triggers, summarize only the newly-truncated span and merge with the existing summary rather than regenerating from scratch. This prevents drift that accumulates when summaries are regenerated wholesale — each regeneration risks losing details the model considers low-priority but the task requires. Structure forces preservation because dedicated sections act as checklists the summarizer must populate, catching silent information loss.

2. Opaque Compression: Reserve this for short sessions where re-fetching costs are low and maximum token savings are required. It produces compressed representations optimized for reconstruction fidelity, achieving 99%+ compression ratios but sacrificing interpretability entirely. The tradeoff matters: there is no way to verify what was preserved without running probe-based evaluation, so never use this when debugging or artifact tracking is critical.

3. Regenerative Full Summary: Use this when summary readability is critical and sessions have clear phase boundaries. It generates detailed structured summaries on each compression trigger. The weakness is cumulative detail loss across repeated cycles — each full regeneration is a fresh pass that may deprioritize details preserved in earlier summaries.

Detailed Topics

Optimize for Tokens-Per-Task, Not Tokens-Per-Request

Measure total tokens consumed from task start to completion, not tokens per individual request. When compression drops file paths, error messages, or decision rationale, the agent must re-explore, re-read files, and re-derive conclusions — wasting far more tokens than the compression saved. A strategy saving 0.5% more tokens per request but causing 20% more re-fetching costs more overall. Track re-fetching frequency as the primary quality signal: if the agent repeatedly asks to re-read files it already processed, compression is too aggressive.

Solve the Artifact Trail Problem First

Artifact trail integrity is often the weakest dimension in compression evaluations (claim-context-compression-factory-benchmark). Address this proactively because general summarization cannot reliably maintain it.

Preserve these categories explicitly in every compression cycle:

• Which files were created (full paths)
• Which files were modified and what changed (include function names, not just file names)
• Which files were read but not changed
• Specific identifiers: function names, variable names, error messages, error codes

Implement a separate artifact index or explicit file-state tracking in agent scaffolding rather than relying on the summarizer to capture these details. Even structured summarization with dedicated file sections struggles with completeness over long sessions.

Structure Summaries with Mandatory Sections

Build structured summaries with explicit sections that prevent silent information loss. Each section acts as a checklist the summarizer must populate, making omissions visible rather than silent.

## Session Intent
[What the user is trying to accomplish]

## Files Modified
- auth.controller.ts: Fixed JWT token generation
- config/redis.ts: Updated connection pooling
- tests/auth.test.ts: Added mock setup for new config

## Decisions Made
- Using Redis connection pool instead of per-request connections
- Retry logic with exponential backoff for transient failures

## Current State
- 14 tests passing, 2 failing
- Remaining: mock setup for session service tests

## Next Steps
1. Fix remaining test failures
2. Run full test suite
3. Update documentation

Adapt sections to the agent's domain. A debugging agent needs "Root Cause" and "Error Messages"; a migration agent needs "Source Schema" and "Target Schema." The structure matters more than the specific sections — any explicit schema outperforms freeform summarization.

Choose Compression Triggers Strategically

When to trigger compression matters as much as how to compress. Select a trigger strategy based on session predictability:

Strategy	Trigger Point	Trade-off
Fixed threshold	70-80% context utilization	Simple but may compress too early
Sliding window	Keep last N turns + summary	Predictable context size
Importance-based	Compress low-relevance sections first	Complex but preserves signal
Task-boundary	Compress at logical task completions	Clean summaries but unpredictable timing

Default to sliding window with structured summaries for coding agents — it provides the best balance of predictability and quality. Use task-boundary triggers when sessions have clear phase transitions (e.g., research then implementation then testing).

Evaluate Compression with Probes, Not Metrics

Traditional metrics like ROUGE or embedding similarity fail to capture functional compression quality. A summary can score high on lexical overlap while missing the one file path the agent needs to continue.

Use probe-based evaluation: after compression, pose questions that test whether critical information survived. If the agent answers correctly, compression preserved the right information. If not, it guesses or hallucinates.

Probe Type	What It Tests	Example Question
Recall	Factual retention	"What was the original error message?"
Artifact	File tracking	"Which files have we modified?"
Continuation	Task planning	"What should we do next?"
Decision	Reasoning chain	"What did we decide about the Redis issue?"

Score Compression Across Six Dimensions

Evaluate compression quality for coding agents across these dimensions. Accuracy and artifact-trail preservation tend to separate methods more clearly than lexical similarity (claim-context-compression-factory-benchmark), so compression needs specialized handling beyond general summarization.

1. Accuracy: Are technical details correct — file paths, function names, error codes?
2. Context Awareness: Does the response reflect current conversation state?
3. Artifact Trail: Does the agent know which files were read or modified?
4. Completeness: Does the response address all parts of the question?
5. Continuity: Can work continue without re-fetching information?
6. Instruction Following: Does the response respect stated constraints?

Practical Guidance

Apply the Three-Phase Compression Workflow for Large Codebases

For codebases or agent systems exceeding context windows, compress through three sequential phases. Each phase narrows context so the next phase operates within budget.

1. Research Phase: Explore architecture diagrams, documentation, and key interfaces. Compress exploration into a structured analysis of components, dependencies, and boundaries. Output: a single research document that replaces raw exploration.

2. Planning Phase: Convert the research document into an implementation specification with function signatures, type definitions, and data flow. A 5M-token codebase compresses to approximately 2,000 words of specification at this stage.

3. Implementation Phase: Execute against the specification. Context stays focused on the spec plus active working files, not raw codebase exploration. This phase rarely needs further compression because the spec is already compact.

Use Example Artifacts as Compression Seeds

When provided with a manual migration example or reference PR, use it as a template to understand the target pattern rather than exploring the codebase from scratch. The example reveals constraints static analysis cannot surface: which invariants must hold, which services break on changes, and what a clean implementation looks like.

This matters most when the agent cannot distinguish essential complexity (business requirements) from accidental complexity (legacy workarounds). The example artifact encodes that distinction implicitly, saving tokens that would otherwise go to trial-and-error exploration.

Implement Anchored Iterative Summarization Step by Step

1. Define explicit summary sections matching the agent's domain (debugging, migration, feature development)
2. On first compression trigger, summarize the truncated history into those sections
3. On subsequent compressions, summarize only newly truncated content — do not re-summarize the existing summary
4. Merge new information into existing sections rather than regenerating them, deduplicating by file path and decision identity
5. Tag which information came from which compression cycle — this enables debugging when summaries drift

Select the Right Approach for the Session Profile

Use anchored iterative summarization when:

• Sessions are long-running (100+ messages)
• File tracking matters (coding, debugging)
• Verification of preserved information is needed

Use opaque compression when:

• Maximum token savings are required
• Sessions are relatively short
• Re-fetching costs are low (e.g., no file system access needed)

Use regenerative summaries when:

• Summary interpretability is critical for human review
• Sessions have clear phase boundaries
• Full context review is acceptable on each compression trigger

Calibrate Compression Ratios by Method

Method	Compression Ratio	Quality Score	Trade-off
Anchored Iterative	98.6%	3.70	Best quality, slightly less compression
Regenerative	98.7%	3.44	Good quality, moderate compression
Opaque	99.3%	3.35	Best compression, quality loss

Use these as source-specific benchmark figures, not universal constants (claim-context-compression-factory-benchmark). For any task where re-fetching costs exist, this tradeoff generally favors structured approaches.

Examples

Example 1: Debugging Session Compression

Original context (89,000 tokens, 178 messages):

• 401 error on /api/auth/login endpoint
• Traced through auth controller, middleware, session store
• Found stale Redis connection
• Fixed connection pooling, added retry logic
• 14 tests passing, 2 failing

Structured summary after compression:

## Session Intent
Debug 401 Unauthorized error on /api/auth/login despite valid credentials.

## Root Cause
Stale Redis connection in session store. JWT generated correctly but session could not be persisted.

## Files Modified
- auth.controller.ts: No changes (read only)
- middleware/cors.ts: No changes (examined)
- config/redis.ts: Fixed connection pooling configuration
- services/session.service.ts: Added retry logic for transient failures
- tests/auth.test.ts: Updated mock setup

## Test Status
14 passing, 2 failing (mock setup issues)

## Next Steps
1. Fix remaining test failures (mock session service)
2. Run full test suite
3. Deploy to staging

Example 2: Probe Response Quality

After compression, asking "What was the original error?":

Good response (structured summarization):

"The original error was a 401 Unauthorized response from the /api/auth/login endpoint. Users received this error with valid credentials. Root cause was stale Redis connection in session store."

Poor response (aggressive compression):

"We were debugging an authentication issue. The login was failing. We fixed some configuration problems."

The structured response preserves endpoint, error code, and root cause. The aggressive response loses all technical detail.

Guidelines

1. Optimize for tokens-per-task, not tokens-per-request
2. Use structured summaries with explicit sections for file tracking
3. Trigger compression at 70-80% context utilization
4. Implement incremental merging rather than full regeneration
5. Test compression quality with probe-based evaluation
6. Track artifact trail separately if file tracking is critical
7. Accept slightly lower compression ratios for better quality retention
8. Monitor re-fetching frequency as a compression quality signal

Gotchas

1. Never compress tool definitions or schemas: Compressing function call schemas, API specs, or tool definitions destroys agent functionality entirely. The agent cannot invoke tools whose parameter names or types have been summarized away. Treat tool definitions as immutable anchors that bypass compression.

2. Compressed summaries hallucinate facts: When an LLM summarizes conversation history, it may introduce plausible-sounding details that never appeared in the original. Always validate compressed output against source material before discarding originals — especially for file paths, error codes, and numeric values that the summarizer may "round" or fabricate.

3. Compression breaks artifact references: File paths, commit SHAs, variable names, and code snippets get paraphrased or dropped during compression. A summary saying "updated the config file" when the agent needs config/redis.ts causes re-exploration. Preserve identifiers verbatim in dedicated sections rather than embedding them in prose.

4. Early turns contain irreplaceable constraints: The first few turns of a session often contain task setup, user constraints, and architectural decisions that cannot be re-derived. Protect early turns from compression or extract their constraints into a persistent preamble that survives all compression cycles.

5. Aggressive ratios compound across cycles: A 95% compression ratio seems safe once, but applying it repeatedly compounds losses. After three cycles at 95%, only 0.0125% of original tokens remain. Calibrate ratios assuming multiple compression cycles, not a single pass.

6. Code and prose need different compression: Prose compresses well because natural language is redundant. Code does not — removing a single token from a function signature or import path can make it useless. Apply domain-specific compression strategies: summarize prose sections aggressively while preserving code blocks and structured data verbatim.

7. Probe-based evaluation gives false confidence: Probes can pass despite critical information being lost, because the probes test only what they ask about. A probe set that checks file names but not function signatures will miss signature loss. Design probes to cover all six evaluation dimensions, and rotate probe sets across evaluation runs to avoid blind spots.

Integration

This skill connects to several others in the collection:

• context-degradation - Compression is a mitigation strategy for degradation
• context-optimization - Compression is one optimization technique among many
• evaluation - Probe-based evaluation applies to compression testing
• memory-systems - Compression relates to scratchpad and summary memory patterns

References

Internal reference:

• Evaluation Framework Reference - Read when: building or calibrating a probe-based evaluation pipeline, or when needing scoring rubrics and LLM judge configuration for compression quality assessment

Related skills in this collection:

• context-degradation - Read when: diagnosing why agent performance drops over long sessions, before applying compression as a mitigation
• context-optimization - Read when: compression alone is insufficient and broader optimization strategies (pruning, caching, routing) are needed
• evaluation - Read when: designing evaluation frameworks beyond compression-specific probes, including general LLM-as-judge methodology

External resources:

• Factory Research: Evaluating Context Compression for AI Agents (December 2025) - Read when: needing benchmark data on compression method comparisons or the 36,000-message evaluation dataset
• Research on LLM-as-judge evaluation methodology (Zheng et al., 2023) - Read when: implementing or validating LLM judge scoring to understand bias patterns and calibration
• Netflix Engineering: "The Infinite Software Crisis" - Three-phase workflow and context compression at scale (AI Summit 2025) - Read when: implementing the three-phase compression workflow for large codebases or understanding production-scale context management

---

Skill Metadata

Created: 2025-12-22 Last Updated: 2026-05-15 Author: Agent Skills for Context Engineering Contributors Version: 1.3.0

Other files in this skill

• references/evaluation-framework.md
• scripts/compression_evaluator.py
• tests/test_compression_evaluator.py

references/evaluation-framework.md (verbatim)

Context Compression Evaluation Framework

This document provides the complete evaluation framework for measuring context compression quality, including probe types, scoring rubrics, and LLM judge configuration.

Probe Types

Recall Probes

Test factual retention of specific details from conversation history.

Structure:

Question: [Ask for specific fact from truncated history]
Expected: [Exact detail that should be preserved]
Scoring: Match accuracy of technical details

Examples:

• "What was the original error message that started this debugging session?"
• "What version of the dependency did we decide to use?"
• "What was the exact command that failed?"

Artifact Probes

Test file tracking and modification awareness.

Structure:

Question: [Ask about files created, modified, or examined]
Expected: [Complete list with change descriptions]
Scoring: Completeness of file list and accuracy of change descriptions

Examples:

• "Which files have we modified? Describe what changed in each."
• "What new files did we create in this session?"
• "Which configuration files did we examine but not change?"

Continuation Probes

Test ability to continue work without re-fetching context.

Structure:

Question: [Ask about next steps or current state]
Expected: [Actionable next steps based on session history]
Scoring: Ability to continue without requesting re-read of files

Examples:

• "What should we do next?"
• "What tests are still failing and why?"
• "What was left incomplete from our last step?"

Decision Probes

Test retention of reasoning chains and decision rationale.

Structure:

Question: [Ask about why a decision was made]
Expected: [Reasoning that led to the decision]
Scoring: Preservation of decision context and alternatives considered

Examples:

• "We discussed options for the Redis issue. What did we decide and why?"
• "Why did we choose connection pooling over per-request connections?"
• "What alternatives did we consider for the authentication fix?"

Scoring Rubrics

Accuracy Dimension

Criterion	Question	Score 0	Score 3	Score 5
accuracy_factual	Are facts, file paths, and technical details correct?	Completely incorrect or fabricated	Mostly accurate with minor errors	Perfectly accurate
accuracy_technical	Are code references and technical concepts correct?	Major technical errors	Generally correct with minor issues	Technically precise

Context Awareness Dimension

Criterion	Question	Score 0	Score 3	Score 5
context_conversation_state	Does the response reflect current conversation state?	No awareness of prior context	General awareness with gaps	Full awareness of conversation history
context_artifact_state	Does the response reflect which files/artifacts were accessed?	No awareness of artifacts	Partial artifact awareness	Complete artifact state awareness

Artifact Trail Dimension

Criterion	Question	Score 0	Score 3	Score 5
artifact_files_created	Does the agent know which files were created?	No knowledge	Knows most files	Perfect knowledge
artifact_files_modified	Does the agent know which files were modified and what changed?	No knowledge	Good knowledge of most modifications	Perfect knowledge of all modifications
artifact_key_details	Does the agent remember function names, variable names, error messages?	No recall	Recalls most key details	Perfect recall

Completeness Dimension

Criterion	Question	Score 0	Score 3	Score 5
completeness_coverage	Does the response address all parts of the question?	Ignores most parts	Addresses most parts	Addresses all parts thoroughly
completeness_depth	Is sufficient detail provided?	Superficial or missing detail	Adequate detail	Comprehensive detail

Continuity Dimension

Criterion	Question	Score 0	Score 3	Score 5
continuity_work_state	Can the agent continue without re-fetching previously accessed information?	Cannot continue without re-fetching all context	Can continue with minimal re-fetching	Can continue seamlessly
continuity_todo_state	Does the agent maintain awareness of pending tasks?	Lost track of all TODOs	Good awareness with some gaps	Perfect task awareness
continuity_reasoning	Does the agent retain rationale behind previous decisions?	No memory of reasoning	Generally remembers reasoning	Excellent retention

Instruction Following Dimension

Criterion	Question	Score 0	Score 3	Score 5
instruction_format	Does the response follow the requested format?	Ignores format	Generally follows format	Perfectly follows format
instruction_constraints	Does the response respect stated constraints?	Ignores constraints	Mostly respects constraints	Fully respects all constraints

LLM Judge Configuration

System Prompt

You are an expert evaluator assessing AI assistant responses in software development conversations.

Your task is to grade responses against specific rubric criteria. For each criterion:
1. Read the criterion question carefully
2. Examine the response for evidence
3. Assign a score from 0-5 based on the scoring guide
4. Provide brief reasoning for your score

Be objective and consistent. Focus on what is present in the response, not what could have been included.

Judge Input Format

{
  "probe_question": "What was the original error message?",
  "model_response": "[Response to evaluate]",
  "compacted_context": "[The compressed context that was provided]",
  "ground_truth": "[Optional: known correct answer]",
  "rubric_criteria": ["accuracy_factual", "accuracy_technical", "context_conversation_state"]
}

Judge Output Format

{
  "criterionResults": [
    {
      "criterionId": "accuracy_factual",
      "score": 5,
      "reasoning": "Response correctly identifies the 401 error, specific endpoint, and root cause."
    }
  ],
  "aggregateScore": 4.8,
  "dimensionScores": {
    "accuracy": 4.9,
    "context_awareness": 4.5,
    "artifact_trail": 3.2,
    "completeness": 5.0,
    "continuity": 4.8,
    "instruction_following": 5.0
  }
}

Benchmark Results Reference

Performance across compression methods (based on 36,000+ messages):

Method	Overall	Accuracy	Context	Artifact	Complete	Continuity	Instruction
Anchored Iterative	3.70	4.04	4.01	2.45	4.44	3.80	4.99
Regenerative	3.44	3.74	3.56	2.33	4.37	3.67	4.95
Opaque	3.35	3.43	3.64	2.19	4.37	3.77	4.92

Key Findings:

1. Accuracy gap: 0.61 points between best and worst methods
2. Context awareness gap: 0.45 points, favoring anchored iterative
3. Artifact trail: Universally weak (2.19-2.45), needs specialized handling
4. Completeness and instruction following: Minimal differentiation

Statistical Considerations

• Differences of 0.26-0.35 points are consistent across task types and session lengths
• Pattern holds for both short and long sessions
• Pattern holds across debugging, feature implementation, and code review tasks
• Sample size: 36,611 messages across hundreds of compression points

Implementation Notes

Probe Generation

Generate probes at each compression point based on truncated history:

1. Extract factual claims for recall probes
2. Extract file operations for artifact probes
3. Extract incomplete tasks for continuation probes
4. Extract decision points for decision probes

Grading Process

1. Feed probe question + model response + compressed context to judge
2. Evaluate against each criterion in rubric
3. Output structured JSON with scores and reasoning
4. Compute dimension scores as weighted averages
5. Compute overall score as unweighted average of dimensions

Blinding

The judge should not know which compression method produced the response being evaluated. This prevents bias toward known methods.

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