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Found 3,749 Skills
Triage failed CI runs on a GitHub-Actions–driven repo — classify regression vs flake vs infra, maintain a single rolling `main-red` issue when main is broken, and point humans at the suspect commit. Use when a workflow fails on `main`, or when a human asks "is main red?", "why did CI fail on main?", "triage this workflow run", "classify this failure". Paired with the consumer repo's `<repo>-pr-lifecycle` skill (PR-side CI triage) and the `web-testing` skill (invoked for `e2e` failures).
This skill should be used when the user asks for markup detection, detect manipulation, image tampering, deepfake detection, document integrity, hidden markup, metadata forensics, EXIF analysis, content authenticity, synthetic media, altered image, C2PA, or provenance verification across documents, images, and video. Guides workflow-level assessment of visual tampering indicators (splicing, cloning, inconsistent lighting or shadows, compression artifacts), metadata and provenance checks (EXIF, hashes, source chain), document revision and hidden markup (tracked changes, comments, invisible text), synthetic-media and deepfake red flags, watermarking and content-credentials concepts, and structured reporting with confidence levels and explicit limitations—not training detection models (ml-research-engineer-safeguards), cryptographic watermark design (cryptographer-specialist), full digital forensics lab attribution or legal conclusions, or blockchain-only tracing unless the user scopes on-chain context.
Analyze an in-progress git branch, compare it with the current master/main using a subagent, derive practical lessons, and generate a concise redo handoff. Use when restarting a messy branch, redoing work cleanly, extracting lessons from current changes, or preparing another agent to verify the handoff, align with the user, and rebuild from the default branch.
Cluster a GitHub issue backlog by root cause into a small set of plan-master issues, redirect children with a standardized comment, and bundle architectural-fix PRs that close clusters atomically. Use when an issue tracker has accumulated dozens of reports that share underlying defects, when asked to triage / consolidate / cluster / dedupe issues, when asked to build a plan series or roadmap from open issues, or when routing a new incoming bug into an existing plan.
Research and draft a response to a GitHub issue or question from an external contributor.
Autonomous NeMo-RL research agent workflow for directed hypothesis testing and open-ended discovery. Guides agents through the full experiment lifecycle: understanding recipes and environments, wiring RL or NeMo-gym runs, launching reproducible baselines and iterations, analyzing results, preserving human oversight, and using git plus TSV logs as the research ledger.
Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.
Generates a daily standup post from GitHub activity and agent session history, and posts it to the mitodl/hq Check-ins discussion. Use when asked to write, generate, or post a daily standup — fetches PR, issue, and code-review activity via the gh CLI, queries recent agent sessions, asks clarifying questions about timing and off-GitHub work, renders the standup in the team's standard format, and posts it as a discussion comment with user confirmation.
Create a pull request in a mitodl GitHub repository using the org's standard PR template. Triggered by /olpr or whenever the user asks to open a pull request in a repo whose remote is under the mitodl GitHub organization. Guides branch inspection, title/body population, and gh pr create invocation.
Detect and analyze fraudulent software distribution repositories masquerading as legitimate security products
This skill should be used when the user asks to "create a changelog", "generate a changelog", "update my changelog", "fill in the changelog", "add a changelog", "CHANGELOG is missing entries", "changelog is out of date", "what's missing from my changelog", "changelog from git history", "write changelog", "release notes", or says "my project needs a CHANGELOG".
Runs ML experiments reproducibly — single runs or autonomous BFS batches. Single mode: isolated venv, time-budgeted, failure-handled, logs to RESEARCH.md. BFS mode (opt-in): designs N hypotheses, runs each for a fixed budget, compares via a single verifiable metric, keeps improvements and git-resets failures — fully autonomous until done. Respects the RESEARCH.md supervision policy for notifications, approvals, and stop limits. Trigger phrases: "run experiment", "train model", "explore design space", "find best config", "autoresearch".