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Anthropic Adds Plugin Evals to Claude Code: 6 Grader Types, a No-Plugin Baseline, and a CI Gate for Skills
Anthropic ships a plugin evals workflow for Claude Code with six grader types, a no-plugin baseline arm, and a CI gate via threshold and cost flags.
Anthropic published a plugin evals workflow for Claude Code, exposed via the "claude plugin eval" command on v2.1.269+. Six grader types exist: regex, tool_used, tool_order, and file_exists are free transcript checks, while llm and baseline invoke a billed judge model. Every case runs with and without the plugin, and the delta (Δ) isolates the plugin's contribution; a Δ near zero with a failing tool_used:Skill grader indicates the skill never triggers. CI gating uses --threshold 0.8, --max-cost-usd, --trust-plugin, and --no-publish flags, with results written to a report.html under evals/results/.
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You're deploying it wrong! TeamCity, Subversion & Web Deploy part 4: Continuous builds with TeamCity
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Top 10 Best Container Security Tools in 2026
2026 roundup ranks Aqua, Sysdig, Prisma Cloud, Wiz, Snyk and CrowdStrike among the ten best container security tools across build-ship-run.
Buyer's guide compares ten container security products by lifecycle fit: Aqua leads full lifecycle, Sysdig leads runtime detection via Falco and eBPF, Wiz offers agentless graph visibility, Snyk covers developer-first shift-left. It notes Trivy and Falco as free production-grade open-source foundations. The guide argues standalone container security is increasingly absorbed into CNAPP platforms from Palo Alto, Wiz and CrowdStrike.
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LLMs and Contextual Integrity
Bruce Schneier highlights two papers: the CIMemories benchmark shows frontier LLMs leak memory attributes up to 69%, and an RL method reduces inappropriate disclosures.
Bruce Schneier discusses contextual integrity in LLMs, referencing the CIMemories benchmark, which uses synthetic profiles with 100+ attributes per user to test whether models with persistent memory disclose sensitive information appropriately. Evaluation showed frontier models exhibit up to 69% attribute-level violations, with GPT-5's violation rate rising from 0.1% to 9.6% across 40 tasks and reaching 25.1% with repeated prompting, showing unstable leakage behavior. A second paper introduces a reinforcement learning framework trained on a synthetic 700-example dataset that substantially reduces inappropriate disclosure while maintaining task performance, with improvements transferring to the human-annotated PrivacyLens benchmark.
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