context-optimization Eval
=========================

Status: review
Score: 77/100
Risk: medium
Decision: manual_review
Policy: review
Reason: Test manually in an isolated workspace and compare against safer alternatives.

Install:
npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill context-optimization

Required checks:
- PASS Task fit: Task wording matches this skill metadata.
- PASS Install path: Install handoff is available.
- PASS Install command safety: standard package or runtime install path
- WARN Trust score: Good trust signals with a few areas worth checking before rollout.
- WARN Audit score: Needs review
- WARN Agent safety gate: Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
- PASS License clarity: MIT
- WARN Permission surface: secrets or environment access, filesystem or document access

Warnings:
- Trust score: Good trust signals with a few areas worth checking before rollout.
- Audit score: Needs review
- Agent safety gate: Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
- Permission surface: secrets or environment access, filesystem or document access
- High-risk permission hints: Secrets or environment access
- Permission surface may require sandboxing
- The SKILL.md excerpt is incomplete at the end, so the full masking rules and edge cases are not fully visible in the provided snippet.
- The Python utilities use heuristic token estimation and simple summarization; the code itself notes that production systems should use model-specific tokenizers and LLM-based summarization.
- The skill does not explicitly address handling untrusted or adversarial content inside tool outputs during masking or compaction, though the stated rules mitigate some risk.
- Quality score needs review
- Permission surface needs review: secrets or environment access, filesystem or document access

Validation plan:
1. Inspect repository, README/SKILL.md, license, and recent commits before production use.
2. Install in an isolated workspace or sandbox with no production secrets available.
3. Run the smallest representative task and record files touched, commands run, network access, and outputs.
4. Compare the selected skill against at least one alternative when the eval status is review or failed.
5. Promote only after the agent reports a successful verification result and unresolved warnings are accepted.

Do not use when:
- teams that need a vendor-supported SLA
- production agents without a repository review
- The SKILL.md excerpt is incomplete at the end, so the full masking rules and edge cases are not fully visible in the provided snippet.
- No OpenAgentSkill engagement data yet
- High-risk permission hints: Secrets or environment access
- Permission surface may require sandboxing
- The Python utilities use heuristic token estimation and simple summarization; the code itself notes that production systems should use model-specific tokenizers and LLM-based summarization.
- The skill does not explicitly address handling untrusted or adversarial content inside tool outputs during masking or compaction, though the stated rules mitigate some risk.

URLs:
- Skill: https://www.openagentskill.com/skills/muratcankoylan-context-optimization
- Audit: https://www.openagentskill.com/skills/muratcankoylan-context-optimization/audit
- JSON: https://www.openagentskill.com/api/agent/evals?slug=muratcankoylan-context-optimization