anthropics / anthropics/claude-code
Supported isolated auth and observable skill-to-agent dispatch for native skill evaluations
- 主要语言
- Python
- 星标
- 145k
- 派生
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- PR 合并指标
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描述
We are preparing ten synthetic native Claude Code skill cases, each repeated
three times. They cover natural activation, near misses, a competing procedure,
multi-step action and authority boundaries. Candidate skill metadata permits
natural invocation; explicit invocation would be a different test.
We need a supported method to run these cases while preserving normal skill
discovery and the real generated Claude-to-Codex integration, with independently
verifiable filesystem confinement and authentication isolation. CLI tool
allowlists, strict MCP configuration and disabling session persistence are
useful settings, but none alone proves those other boundaries.
Please identify the supported interfaces for:
1. Authentication to a disposable evaluation session through an isolated
broker, with no copied credentials and no model/worker credential access.
2. Reading the actual installed skill identity and invocation event without
injecting the skill text into the test prompt or forcing explicit invocation.
3. Observing real generated Codex dispatch, its selected route and effective
permissions, and correlating it with the frozen work packet.
4. Retaining only invocation/tool-name/outcome metadata and independently
checked artifact hashes, without keeping raw transcripts or auth material.
We will first run one synthetic canary after these prerequisites are verified.
Thirty case descriptions, static metadata tests or disabled-tool refusal cases
will not be represented as thirty successful native observations.
贡献指南
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调研方向
No files, tests, or entry points are named. Begin by locating the supported evaluation-session, skill-discovery, dispatch-observation, and metadata-retention interfaces, then verify whether they provide the requested isolation and correlation guarantees. Done means one synthetic canary produces independently checked metadata without raw transcripts or authentication material.
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评估
- 技术栈
- python
- 领域
- cli, security, testing
- Issue 类型
- 功能
- 难度
- 5/5
- 预计耗时
- 一周以上
- 活跃度
- 活跃
- 描述清晰度
- 需要澄清
- 新手友好度
- 25/100