OpenAgentSkill
GitHub200+

Pre-install eval

context-optimization Eval report.

A machine-readable install decision for agents: task fit, Trust Score, Audit Score, install safety, permission surface, and a concrete validation plan before this skill touches a workspace.

Needs reviewMedium riskREVIEW Policy
74
Eval report
74
Trust
82
Audit
50
Agent safety

manual review

Test manually in an isolated workspace and compare against safer alternatives.

Required gates

Checks an agent must pass before install

Open JSON

Task fit

84

pass

Task wording matches this skill metadata.

  • Evaluate context-optimization before installing it in an agent workflow
  • automation
  • RAG and knowledge workflows; Claude Code teams; teams that value GitHub adoption signals

Install path

92

pass

Install handoff is available.

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

Install command safety

92

pass

standard package or runtime install path

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

Trust score

74

warn

Good trust signals with a few areas worth checking before rollout.

  • Strong shortlist
  • 18K GitHub stars
  • MIT

Audit score

82

warn

Needs review

  • Permission surface may require sandboxing

Agent safety gate

50

warn

Sparse or mixed signals. Useful for discovery, but not for autonomous installation.

  • Test manually in an isolated workspace and compare against safer alternatives.
  • High-risk permission hints: Secrets or environment access

License clarity

86

pass

MIT

  • MIT

Permission surface

60

warn

secrets or environment access, filesystem or document access

  • Network access: medium
  • Filesystem access: medium
  • Secrets or environment access: high

Validation plan

What the agent should do next

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

Do not use when

Conditions that require another skill

  • 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.
  • 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.

Supporting checks

Trust signals behind the decision

README/SKILL.md completeness

pass

86

Metadata includes enough usage and workflow context

Recent maintenance

pass

88

3mo since push

Alternatives available

pass

82

Alternative skills are available for comparison.