anthropics / anthropics/claude-code

Supported isolated auth and observable skill-to-agent dispatch for native skill evaluations

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area:permissions area:skills question
Dominant language
Python
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Description

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.

Contributor guide

No contributing guide indexed for this repository

Research direction

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.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
cli, security, testing
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Active
Clarity
Needs clarification
Newbie friendliness
25/100

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