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Writes and reviews Lean 4 proofs, machine-checked arguments for mathematics or specifications. Follows Mathlib conventions to organize definitions and lemmas, fill proof gaps, refactor lengthy proofs, and establish automated checks for unproved claims and style.
Programmatically clear WP Rocket cache from a third-party plugin / theme when data changes — the public rocket_clean_* function family (rocket_clean_post, rocket_clean_files, rocket_clean_term, rocket_clean_user, rocket_clean_home, rocket_clean_minify, rocket_clean_cache_busting, rocket_clean_domain, rocket_clean_cache_dir). Critical detection rule — WP Rocket is a PAID plugin not on Packagist; always feature-detect via function_exists('rocket_clean_post') OR defined('WP_ROCKET_VERSION') before calling, since not every site has it. Never raw-unlink the cache directory or call wp_cache_flush() expecting it to clear WP Rocket — wp_cache_flush is WP object cache, WP Rocket is FILE cache. The before_*_clean_* / after_*_clean_* action lifecycle hooks fire around every clean — useful for audit logging, monitoring, custom invalidation chains. Use when integrating cache invalidation in a companion plugin, WC integration, custom data plugin. Triggers on rocket_clean_, before_rocket_clean, af...
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Develop mathematical proofs in Lean, a system that checks formal proofs, continue unfinished projects, or audit existing results. Check that the theorem matches the original claim, examine its assumptions, and prepare sources and commands for reproduction.
Turn mathematical claims into definitions, lemmas, and proofs for Lean 4 and Mathlib. Check proofs with Lean’s trusted core, separately review whether the formal statement matches the original claim, and record verification evidence.
Clean spoken recordings by transcribing words, identifying filler and false starts, and joining cuts with short audio fades. Preview proposed edits, reduce breaths, repair noise-gate clicks, and optionally use Cleanvoice for additional cloud processing.
Automate Campaign Cleaner operations through Rube MCP and Composio. The skill discovers available tools, checks the service connection, and uses current input requirements to carry out the requested workflow.
Automate Campaign Cleaner operations through Rube MCP and Composio. The skill discovers available tools, checks the service connection, and uses current input requirements to carry out the requested workflow.
Find and identify the CCSGameRules_PostCleanUp function in CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the post-round entity-cleanup routine. It is non-virtual and has no unique string of its own, so it is found via the CCSGameRules_ResetRound predecessor: PostCleanUp is the entity-cleanup call ResetRound makes. Emits a unique IDA-style byte signature and a func YAML. Mirrors the find-CCSGameRules_PostCleanUp.py preprocessor (LLM_DECOMPILE CCSGameRules_ResetRound). Trigger: CCSGameRules_PostCleanUp, PostCleanUp, CGameRules::PostCleanUp
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Integrate files generated during MSBuild into compilation or build output. Address missing generated source and content by choosing target timing, using the configured intermediate directory, and registering files so Clean can remove them.
Remove AI-generated code smells (slop) from branch changes or an explicit file list. Locks behavior with regression tests FIRST, then runs categorized cleanup via parallel `deep` agents in batches of 5, then verifies with quality gates. Covers 10 slop categories including performance equivalences, excessive complexity (object annotations, if/elif variant chains), and oversized modules (250+ pure LOC with mandatory modular refactoring). MUST USE when the user asks to "remove slop", "clean AI code", "deslop", "clean up AI-generated code", "remove AI slop", or wants to clean up AI-generated patterns from recent changes. Triggers - "remove ai slops", "clean ai code", "deslop", "cleanup AI generated", "remove AI slop", "clean up AI-generated code", "strip slop", "ai-slop cleanup".
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Prepare spreadsheet data for analysis by finding extra spaces, inconsistent capitalization, numbers stored as text, mixed date formats, and duplicates. Review proposed fixes, apply traceable formula-based changes, and compare results before and after cleanup.
Clean up Git worktrees, separate working folders for branches, by identifying merged branches and reviewing unmerged ones. Preview deletions, preserve protected branches, and receive a report of removed folders and reclaimed disk space.
Find Git branches that have been merged or inactive for a chosen period. Preview cleanup candidates, exclude protected branches, and delete local or remote branches using a selected base branch and confirmation options.
Integrate ManagedCode.Orleans.Graph into an Orleans-based .NET application for grain-call policy enforcement, deadlock detection, live-call telemetry, and Mermaid graph diagnostics. USE FOR: ManagedCode.Orleans.Graph integration; allowed grain transitions; Orleans call filters; live policy graphs; reviewing Orleans call-cycle risk. DO NOT USE FOR: generic graph data modeling or traversal unrelated to Orleans calls. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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Identify unused AWS resources, estimate potential savings, and prepare cleanup scripts with preview steps. Covers storage retention policies and scheduled cleanup so teams can review dependencies before removing resources and track the resulting savings.
Clean up local Git branches and remote connections left after pull request reviews. The workflow checks unfinished work, preserves main and the current branch, and requests confirmation before force-deleting branches that have not been merged.
Turn a product idea into a Lean Canvas, a nine-part outline of business assumptions. Define customer problems, solutions, value, channels, revenue, costs, and metrics, then identify risky assumptions and suggest experiments to test them.
Analyze disk usage on macOS or Windows, identify large folders, and classify cleanup candidates by the care they need. Generate an interactive HTML report with estimated space savings, cleanup guidance, and optional browser controls for deleting selected files.
Maintain data retention in ClawHub's Convex tables by classifying each table, defining expiration fields, and bounding cleanup jobs. Guide changes to temporary data and expired authentication records, with focused tests and runtime verification.
Find merged or inactive Git branches, preview cleanup candidates, and delete selected branches after confirmation. Choose a comparison branch, protect important branches with configurable rules, and optionally include remote branches.
Write and repair custom MSBuild targets, the steps that organize build tasks. Preserve existing build sequences, choose appropriate insertion points, distinguish returned results from rebuild checks, and register generated files for cleanup.
Clean up a feature branch after its pull request is merged into main. Switch to main, pull the latest changes, and delete both local and remote branches, including branches merged through a squash merge.
Review how the lean-ctx ctx_* MCP tools performed in the current session and file upstream issues for confirmed problems. Use when the user asks to review/audit lean-ctx tool performance, says "lean-ctx review", asks why ctx_* tools failed or fell back to native tools, or wants to report a lean-ctx bug.
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Run inference on Fireworks AI — direct, through the Leanmcp AI Gateway proxy (aigateway.leanmcp.com) for observability, or via LiteLLM / the OpenAI SDK / raw HTTP. Covers the endpoint-and-key matrix for each route, serverless vs dedicated deployments and their cold starts, browsing and filtering the model catalogue, reasoning models that return content:null with the answer in reasoning_content, function calling, concurrency and load testing, and runner scripts where many models share one protocol with a --use-proxy toggle. Use this skill whenever the user mentions Fireworks, FIREWORKS_API_KEY, api.fireworks.ai, fireworks_ai/ model ids, accounts/fireworks/models/..., a Fireworks deployment id, the Leanmcp gateway or LEANMCP_API_KEY, app.leanmcp.com observability, or gpt-oss / kimi / glm / qwen / deepseek served on Fireworks; wants to route LLM calls through a proxy for logging or cost tracking; is debugging empty responses, 404s, scope errors, cold starts, or "the gateway isn't working"; wants to pick a Fireworks model with tool calling; or wants to sweep several models over one benchmark. Reach for it even on vague asks like "why is this model returning nothing" or "set up inference for these models" when Fireworks is in play.
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