AI Agent Workflow Collection

Add one or more AI models to the language model constants file, researching specs from official docs.

Check GitHub Actions workflow runs from the past day, identify severe or consistent failures, and file an issue if actionable problems are found.

Map a minified error stack trace from a production Dyad build back to original source locations using source maps.

Identify and fix flaky E2E tests by running them repeatedly and investigating failures.

Automatically gather flaky E2E tests from recent CI runs on the main branch and from recent PRs by wwwillchen/keppo-bot/dyad-assistant, then deflake them.

Turn customer feedback (usually an email) into discrete GitHub issues. Checks for duplicates, proposes new issues for approval, creates them, and drafts a reply email.

Multi-agent code review system that spawns three independent Claude sub-agents to review PR diffs. Each agent receives files in different randomized order to reduce ordering bias. One agent focuses specifically on code health and maintainability. Issues are validated using reasoned analysis rather than simple vote counting. Reports merge verdict (YES / NOT SURE / NO). Automatically deduplicates against existing PR comments. Always posts a summary (even if no new issues), with low priority issues in a collapsible section.

Create a plan collaboratively with the user, then convert the approved plan into a GitHub issue.

Address all outstanding issues on a GitHub Pull Request by handling both review comments and failing CI checks.

Rebase the current branch on the latest upstream changes, resolve conflicts, and push.

Record a visual demonstration of the key feature of this PR using screenshots and add it as a new comment to the PR.

Promote the latest pre-release to a stable release by creating a release branch, bumping the version, and pushing.

Team-based PR review using Claude Code swarm. Spawns three specialized teammates (correctness expert, code health expert, UX wizard) who review the PR diff, discuss findings with each other, and reach consensus on real issues. Posts a summary with merge verdict and inline comments for HIGH/MEDIUM issues.

Swarm planning session with PM, UX, and Engineering agents who debate an idea, ask clarifying questions, and produce a detailed spec written to plans/$plan-name.md.

Review the current session for errors, issues, snags, and hard-won knowledge, then update the rules/ files (or AGENTS.md if no suitable rule file exists) with actionable learnings.

Debug a specific session by inspecting its full event chain (PreToolUse, PostToolUse, Stop, SubagentStop, Compaction, APIError, TurnDuration, Notification events), agent hierarchy (recursive parent/child tree with subagenttype and depth), token usage with compaction baselines, workflow intelligence data (orchestration DAG, error propagation by depth), and session metadata (thinkingblocks, turncount, totalturndurationms).