Summary It frames agent requests around context, deliverable, and operating constraints.
The details
The framework defines three components: what the agent should look at, produce, and follow as rules.
One example reads designated-folder notes and creates summary.md: a one-page, simple-language summary in short bullets.
It recommends copying task-relevant files into a clean workspace instead of granting whole-desktop access for focus, token cost, and speed.
Evidence is creator-supplied instruction; no first-party DeepSeek Harness documentation, release record, benchmark, or technical specification was supplied.
Products and workflows worth trying, with limitations and direct links.
Coding Agents & Developer Tools
Six agent projects target context, delegation, and access
Summary A curated set of open-source projects addresses persistent context, orchestration, terminal-output, security, retrieval, and web-access constraints.
The details
OthmanAdi/planning-with-files keeps task_plan.md, findings.md, and progress.md in a project, using hooks to bring them into agent context.
amElnagdy/delegate-skills is presented as a delegation layer for installed coding agents, including Claude Code, Codex, and Cursor, with 18 install-selectable skills.
rtk-ai/rtk filters terminal output before it reaches an agent.
moonlight-lupin/agent-skills includes Skill Retrieval; its documentation reportedly measures 11K tokens before retrieval versus 2.3K after.
google/mantis provides security-review skills.
Panniantong/Agent-Reach is presented as research access across more than 15 platforms.
Summary Anthropic reports ten Claude alignment-failure gains; aggregate usage analysis opens to external researchers.
The details
Under monitoring, Claude searched literature, proposed methods and training data, trained target models, and tested alignment failures individually.
Across ten categories, target benchmarks improved without general-capability regressions; methods held on withheld evaluations, Petri, and models 4.7 times larger.
Claude Sonnet 5 tested more than 50 interventions against an early Claude Opus 4.8 checkpoint in 60 hours.
Anthropic says the winning method used just over 2,000 examples and was roughly 15,000 times more efficient than production alignment.
Three external groups analyzed roughly 250,000 Claude.ai and Claude Code conversations from April to May 2026 through Anthropic Insights.
Anthropic says it released aggregate data for each project.