Context Compression Strategies workflow skill. Use this skill when the user needs When agent sessions generate millions of tokens of conversation history, compression becomes mandatory. The naive approach is aggressive compression to minimize tokens per request and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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name
context-compression
description
Context Compression Strategies workflow skill. Use this skill when the user needs When agent sessions generate millions of tokens of conversation history, compression becomes mandatory. The naive approach is aggressive compression to minimize tokens per request and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/context-compression from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
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.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Concepts, Detailed Topics, Session Intent, Files Modified, Decisions Made, Current State.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
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
Use when the request clearly matches the imported source intent: When agent sessions generate millions of tokens of conversation history, compression becomes mandatory. The naive approach is aggressive compression to minimize tokens per request.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Fix remaining test failures
Run full test suite
Update documentation
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
Imported Workflow Notes
Imported: Next Steps
Fix remaining test failures
Run full test suite
Update documentation
This structure prevents silent loss of file paths or decisions because each section must be explicitly addressed.
### Compression Trigger Strategies
When to trigger compression matters as much as how to compress:
| 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 |
The sliding window approach with structured summaries provides the best balance of predictability and quality for most coding agent use cases.
### Probe-Based Evaluation
Traditional metrics like ROUGE or embedding similarity fail to capture functional compression quality. A summary may score high on lexical overlap while missing the one file path the agent needs.
Probe-based evaluation directly measures functional quality by asking questions after compression:
| 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?" |
If compression preserved the right information, the agent answers correctly. If not, it guesses or hallucinates.
### Evaluation Dimensions
Six dimensions capture compression quality for coding agents:
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?
Accuracy shows the largest variation between compression methods (0.6 point gap). Artifact trail is universally weak (2.2-2.5 range).
#### Imported: 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.
Imported: Core Concepts
Context compression trades token savings against information loss. Three production-ready approaches exist:
Anchored Iterative Summarization: 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. Structure forces preservation by dedicating sections to specific information types.
Opaque Compression: Produce compressed representations optimized for reconstruction fidelity. Achieves highest compression ratios (99%+) but sacrifices interpretability. Cannot verify what was preserved.
Regenerative Full Summary: Generate detailed structured summaries on each compression. Produces readable output but may lose details across repeated compression cycles due to full regeneration rather than incremental merging.
The critical insight: structure forces preservation. Dedicated sections act as checklists that the summarizer must populate, preventing silent information drift.
Examples
Example 1: Ask for the upstream workflow directly
Use @context-compression to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @context-compression against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @context-compression for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @context-compression using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Imported Usage Notes
Imported: 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:
## Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
- Optimize for tokens-per-task, not tokens-per-request
- Use structured summaries with explicit sections for file tracking
- Trigger compression at 70-80% context utilization
- Implement incremental merging rather than full regeneration
- Test compression quality with probe-based evaluation
- Track artifact trail separately if file tracking is critical
- Accept slightly lower compression ratios for better quality retention
### Imported Operating Notes#### Imported: Guidelines1. 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
## Troubleshooting### Problem: The operator skipped the imported context and answered too generically**Symptoms:** The result ignores the upstream workflow in `plugins/antigravity-awesome-skills-claude/skills/context-compression`, fails to mention provenance, or does not use any copied source files at all.
**Solution:** Re-open `metadata.json`, `ORIGIN.md`, and the most relevant copied upstream files. Check the `external_source` block first, then restate the provenance before continuing.
### Problem: The imported workflow feels incomplete during review**Symptoms:** Reviewers can see the generated `SKILL.md`, but they cannot quickly tell which references, examples, or scripts matter for the current task.
**Solution:** Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
### Problem: The task drifted into a different specialization**Symptoms:** The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
**Solution:** Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
## Related Skills-`@00-andruia-consultant` - Use when the work is better handled by that native specialization after this imported skill establishes context.
-`@00-andruia-consultant-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context.
-`@10-andruia-skill-smith` - Use when the work is better handled by that native specialization after this imported skill establishes context.
-`@10-andruia-skill-smith-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context.
## Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
| Resource family | What it gives the reviewer | Example path |
| --- | --- | --- |
| `references` | copied reference notes, guides, or background material from upstream | `references/n/a` |
| `examples` | worked examples or reusable prompts copied from upstream | `examples/n/a` |
| `scripts` | upstream helper scripts that change execution or validation | `scripts/n/a` |
| `agents` | routing or delegation notes that are genuinely part of the imported package | `agents/n/a` |
| `assets` | supporting assets or schemas copied from the source package | `assets/n/a` |
### Imported Reference Notes#### Imported: References
Internal reference:
- Evaluation Framework Reference - Detailed probe types and scoring rubrics
Related skills in this collection:
- context-degradation - Understanding what compression prevents
- context-optimization - Broader optimization strategies
- evaluation - Building evaluation frameworks
External resources:
- Factory Research: Evaluating Context Compression for AI Agents (December 2025)
- Research on LLM-as-judge evaluation methodology (Zheng et al., 2023)
- Netflix Engineering: "The Infinite Software Crisis" - Three-phase workflow and context compression at scale (AI Summit 2025)
---
#### Imported: Detailed Topics### Why Tokens-Per-Task Matters
Traditional compression metrics target tokens-per-request. This is the wrong optimization. When compression loses critical details like file paths or error messages, the agent must re-fetch information, re-explore approaches, and waste tokens recovering context.
The right metric is tokens-per-task: total tokens consumed from task start to completion. A compression strategy saving 0.5% more tokens but causing 20% more re-fetching costs more overall.
### The Artifact Trail Problem
Artifact trail integrity is the weakest dimension across all compression methods, scoring 2.2-2.5 out of 5.0 in evaluations. Even structured summarization with explicit file sections struggles to maintain complete file tracking across long sessions.
Coding agents need to know:
- Which files were created
- Which files were modified and what changed
- Which files were read but not changed
- Function names, variable names, error messages
This problem likely requires specialized handling beyond general summarization: a separate artifact index or explicit file-state tracking in agent scaffolding.
### Structured Summary Sections
Effective structured summaries include explicit sections:
```markdown
#### Imported: Session Intent
[What the user is trying to accomplish]
#### Imported: 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
#### Imported: Decisions Made
- Using Redis connection pool instead of per-request connections
- Retry logic with exponential backoff for transient failures
#### Imported: Current State
- 14 tests passing, 2 failing
- Remaining: mock setup for session service tests
#### Imported: Practical Guidance
### Three-Phase Compression Workflow
For large codebases or agent systems exceeding context windows, apply compression through three phases:
1. **Research Phase**: Produce a research document from architecture diagrams, documentation, and key interfaces. Compress exploration into a structured analysis of components and dependencies. Output: single research document.
2. **Planning Phase**: Convert research into implementation specification with function signatures, type definitions, and data flow. A 5M token codebase compresses to approximately 2,000 words of specification.
3. **Implementation Phase**: Execute against the specification. Context remains focused on the spec rather than raw codebase exploration.
### Using Example Artifacts as Seeds
When provided with a manual migration example or reference PR, use it as a template to understand the target pattern. The example reveals constraints that static analysis cannot surface: which invariants must hold, which services break on changes, and what a clean migration looks like.
This is particularly important when the agent cannot distinguish essential complexity (business requirements) from accidental complexity (legacy workarounds). The example artifact encodes that distinction.
### Implementing Anchored Iterative Summarization
1. Define explicit summary sections matching your agent's needs
2. On first compression trigger, summarize truncated history into sections
3. On subsequent compressions, summarize only new truncated content
4. Merge new summary into existing sections rather than regenerating
5. Track which information came from which compression cycle for debugging
### When to Use Each Approach
**Use anchored iterative summarization when:**
- Sessions are long-running (100+ messages)
- File tracking matters (coding, debugging)
- You need to verify what was preserved
**Use opaque compression when:**
- Maximum token savings required
- Sessions are relatively short
- Re-fetching costs are low
**Use regenerative summaries when:**
- Summary interpretability is critical
- Sessions have clear phase boundaries
- Full context review is acceptable on each compression
### Compression Ratio Considerations
| 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 |
The 0.7% additional tokens retained by structured summarization buys 0.35 quality points. For any task where re-fetching costs matter, this trade-off favors structured approaches.
#### Imported: Session Intent
Debug 401 Unauthorized error on /api/auth/login despite valid credentials.
#### Imported: Root Cause
Stale Redis connection in session store. JWT generated correctly but session could not be persisted.
#### Imported: 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
#### Imported: Test Status
14 passing, 2 failing (mock setup issues)
#### Imported: 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
#### Imported: Skill Metadata
**Created**: 2025-12-22
**Last Updated**: 2025-12-26
**Author**: Agent Skills for Context Engineering Contributors
**Version**: 1.1.0
#### Imported: Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
