Common Context Optimization
Maximize context window efficiency, reduce latency, and prevent lost-in-middle issues through strategic masking and compaction. Use when token budgets are tight, tool outputs overflow the context, conversations drift from intent, or latency spikes from cache misses.
Install
$ agentstack add skill-hoangnguyen0403-agent-skills-standard-common-context-optimization ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-hoangnguyen0403-agent-skills-standard-common-context-optimization) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Priority: P1 (OPTIMIZATION)
1. Observation Masking (Noise Reduction)
Problem: Large tool outputs (logs, JSON lists) overwhelm context and degrade reasoning. Solution: Replace raw output with semantic summaries after consumption.
- Identify outputs exceeding 50 lines or 1 KB.
- Extract critical data points immediately.
- Mask by rewriting history to replace raw data with summary placeholder.
- See
references/masking.mdfor patterns.
See [implementation examples](references/implementation.md) for masking patterns.
2. Context Compaction (State Preservation)
Problem: Long conversations drift from original intent. Solution: Recursive summarization that preserves State over Dialogue.
- Trigger compaction every 10 turns or 8k tokens.
- Compact:
- Keep: User Goal, Active Task, Current Errors, Key Decisions.
- Drop: Chat chit-chat, intermediate tool calls, corrected assumptions.
- Format: Update System Prompt or Memory File with compacted state.
- See
references/compaction.mdfor algorithms.
See [implementation examples](references/implementation.md) for compacted state format.
3. KV-Cache Awareness (Latency)
Goal: Maximize pre-fill cache hits.
- Static Prefix: Enforce strict ordering — System -> Tools -> RAG -> User.
- Append-Only: Never insert into middle of history; append new turns only.
References
- [Observation Masking Patterns](references/masking.md)
- [Compaction Algorithms](references/compaction.md)
Anti-Patterns
- No raw tool dumps: Mask large outputs immediately after extracting data.
- No unbounded growth: Compact every 10 turns to preserve intent over dialogue.
- No middle insertions: Append-only history maximizes KV cache hits.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: HoangNguyen0403
- Source: HoangNguyen0403/agent-skills-standard
- License: Apache-2.0
- Homepage: https://www.npmjs.com/package/agent-skills-standard
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.