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Proactively draft and create pull requests by inferring title, refs, and body from git context, then confirm and open via gh CLI.

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Diagnose and fix CI failures without leaving your editor.

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Delete local git branches whose remotes are gone, after verifying via gh CLI that each was actually merged.

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Guide authors through a structured self-review before requesting peer review.

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AI repo versioning and changelogs via release-please — never edit CHANGELOG.md or pyproject version by hand. Use when the user asks about releases, version bumps, changelogs, or preparing a release.

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Guide branch creation, commit messages, and titles so the repo’s semantic workflow is enforced

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Backport fixes from main onto release/YYYY.WW in the ai repo with cherry-pick and rebase merge so release-please changelogs stay correct. Use when hotfixing a release branch, cherry-picking to release/*, or preparing a release backport PR.

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Triage and fix Dependabot alerts and CodeQL findings in the ai repository. Use when the user asks to address security vulnerabilities, fix Dependabot alerts, resolve CodeQL issues, or do security maintenance.

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Reproduce ai-repo PR checks locally with Poe and CI scripts, including per-package typecheck and coverage behavior. Use when validating changes before push or when user asks which local commands match CI.

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Deploy a new LLM model via LiteLLM (GitOps) and/or Azure (Terraform + node backend), add it to the ai toolkit, create the required pull requests, and verify end-to-end. Use when rolling out a model to any environment, adding a model to the toolkit, troubleshooting "model not available" errors, or finding pricing/token limits/model ids for any provider.

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Manage Python dependencies, lock files, and project environments using Poetry.

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After pushing code or creating a PR, automatically wait for CI checks to complete, fix failures, address Bugbot findings, and re-push in a loop until the PR is green. Use when the user pushes to a PR, creates a PR, or asks to get a PR to green.

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Guide reviewers through systematic, constructive peer review of a pull request.

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Write focused, deterministic, well-documented pytest tests for AI-authored code.

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Refactor Python code to improve quality while keeping functionality intact. Use when cleaning up complexity, removing duplication, or tightening structure.

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Fix all Python lint violations and enforce consistent formatting in one command.

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Tabular and numerical data analysis with descriptive statistics and insights. Use when the user provides data, tables, CSVs, or numbers and wants analysis.

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Financial factsheet analysis with key metrics extraction and investment rationale

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Contract risk analysis, obligation mapping, and clause review. Use when the user asks to review, analyze, or check a contract or legal document.

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ALWAYS use this skill whenever you would otherwise ask the user a question in free-form chat -- for clarifications, confirmations (especially destructive actions), missing parameters, multiple-choice decisions, or structured form input. Elicitations are routed through the Unique AI Platform UI via `unique-cli elicit create` + `elicit wait` (the default polling pattern) or a single supervised `unique-cli elicit ask` call (only when your agent instructions say your environment supervises the wait) so the user gets a proper structured prompt and you get a structured answer back. Do NOT ask the user in plain chat when you can use this skill instead.

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ALWAYS use this skill when the user asks to schedule, automate, set up recurring tasks, create a cron job, or anything involving timed / periodic execution. Do NOT use the local system crontab — scheduled tasks are stored on the Unique AI Platform and executed by a Kubernetes CronJob, not locally. Scheduled tasks always run on the current assistant (`$UNIQUE_ASSISTANT_ID`); never ask the user which assistant to use, neither in chat nor via `unique-cli elicit ask`. This skill covers creating, listing, updating, enabling, disabling, and deleting scheduled tasks via the unique-cli schedule command.

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Invoke connected Unique spaces/subagents through the unique-cli subagent command. Use when the workspace exposes connected-space tools and you need to delegate a question or task to one of those configured assistants.

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Search the public web and crawl URLs via the Unique AI Platform's `unique-cli web-search` command, with automatic per-turn citation tracking so cited web facts render as clickable external reference chips and `<sup>N</sup>` footnotes on the Unique platform. ALWAYS use this skill when the user asks to search the public web, research a topic online, look up news/articles/regulations, fetch the contents of one or more URLs, or perform any "two-phase" web research (search → review snippets → crawl selected URLs for full content). Routes through the Unique AI Platform's `/web-search-api` endpoints, so the engine (Google, Brave, Tavily, Jina, Firecrawl, …) and crawler are resolved server-side from the current environment — you never need to manage engine API keys yourself. Use this instead of fabricating answers from training data whenever fresh / source-cited information is needed. Do NOT use this skill for the internal knowledge base (`unique-cli search`) or for arbitrary tool execution (`unique-cli mcp`).

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Implement comprehensive error handling for Python code paths to keep services resilient and user-friendly. Use when failures are currently silent or exceptions leak through.

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