Automates GitHub issue queues into merged code changes with traceable specs and PR outcomes.
npx skills add https://github.com/berlinguyinca/autospec --skill autospec-run
Please help me install this Agent Skill. Skill: autospec-run Source: https://github.com/berlinguyinca/autospec/tree/main/skills/autospec-run Command: npx skills add https://github.com/berlinguyinca/autospec --skill autospec-run
Autospec-run turns a pre-built GitHub issue implementation queue into shipped, reviewable, explainable code changes without requiring the operator to micromanage every PR.
auto-implement issues, runs the implement→test→PR→self-review→admin-squash-merge loop, then immediately continues as new merges unblock the queue.ctx:* and reasoning:* ceilings using ~/.autospec/model-profiles.yml and reports deferred items at run end.Run autospec-run on your repo’s populated queue with the command: /autospec-run --profile claude-sonnet-cloud
High-intent search queries and answers about installing and using this skill.
You can autonomously implement and merge queued GitHub issues by running a monitor loop that claims ready issues, runs implementation, testing, PR creation, self-review, and admin squash-merge. This requires a populated auto-implement issue queue and path conflict checks before claiming.
Autonomous code review and issue triage with CI gates works by enforcing dependency closure, rebase-and-retest before merge, and doc drift checks. The system claims issues only when dependencies are closed and path conflicts are clear, then performs admin squash-merge to finalize changes.
Yes, you can run autonomous implementation across multiple machines using distributed queue coordination. This uses GitHub labels and a run-state comment/heartbeat mechanism to safely coordinate claims between workers, preventing conflicts during the implement and merge loop.
Yes, you need gh, jq, git, curl, and ollama installed to run autonomous implementation. These tools support the CLI operations, JSON parsing, repository interactions, and local model profile filtering required to process the auto-implement queue.
Model profile filtering for autonomous issue implementation is an optional mechanism that filters candidate issues by `ctx:*` and `reasoning:*` ceilings using a configuration file. It ensures tasks match your model's capabilities and reports deferred items at the end of the run.
You run autonomous implementation with Claude Code, OpenCode, or Codex CLI by passing a profile flag to the execution command. The system applies the implementation portion of the workflow across these multiple harnesses, monitoring phases 4–6 with autonomous merges.
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