oimiragieo

Creates structured plans from requirements. Generates comprehensive plans with steps, dependencies, risks, and success criteria. Coordinates with specialist agents for planning input and validates plan completeness. Uses template-renderer for formatted output.

Create, validate, and convert skills for the agent ecosystem. Enforces standardized structure for consistency. Enables self-evolution by creating new skills on demand, converting MCP servers and codebases to skills.

Research-backed skill refresh workflow for updating existing skills with TDD checkpoints, memory-aware integration, and EVOLVE/reflection trigger handling.

Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.

Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.

N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.

Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze

Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.

Compress an MCP tools/list manifest so downstream agents see shorter tool descriptions without losing inputSchema semantics. Use before forwarding a large tools/list to a secondary agent or storing it in context.

Pre-flight check before sending an expensive LLM prompt. Returns a verdict (send_as_is, send_compressed, warn_context_limit, clear_first), the compressed prompt body inline, a cost preview against on-demand list pricing, and a cache hit likelihood. Use this skill whenever an agent is about to submit a prompt above ~1000 tokens or when context fill is unknown. Trigger phrases include "estimate cost before sending", "should I compress this prompt", "am I about to run out of context", "preview compression savings".

Compresses a git diff before review so noise (lockfile bumps, generated files, whitespace churn) doesn't crowd out the actual logic changes. Use this skill whenever the user asks you to review a pull request, explain a diff, summarize changes, or comment on recent commits. Trigger phrases include "review this PR", "what changed", "explain this diff", "summarize the changes", "look at my diff", "check this branch against main". Uses gotcontext's code-aware compression at fidelity=detailed — detailed because a character dropped in a security fix matters more than a character dropped in prose.

Compress a session's conversation history into a portable summary that can be re-injected after /clear. Use this skill when context is filling up and the agent needs to recover state after clearing. Works even when Claude Code's own auto-compact has failed because gotcontext runs the summarization on its own infra. Trigger phrases include "summarize this session", "I need to clear context but keep what we built", "compact failed help me recover", "save this conversation before reset".

Bulk compression of a file corpus — up to 50 documents per call — for later queries rather than one-off reads. Use this skill whenever the user wants to pre-process a directory, a support-ticket dump, meeting notes, or any sizable corpus before Q&A. Trigger phrases include "compress all these files", "pre-process this corpus", "shrink everything in /path", "bulk-compress for Q&A", or when onboarding Claude to a large project that needs a lot of ingested context. Submits an async job to gotcontext's batch queue and returns a job id; the job processes in the background with per-item error reporting so one bad file doesn't block the batch. Pro, Team, or Enterprise plan required.

Extracts the public API of a codebase — class names, function signatures, exported types, module docstrings — without loading every file. Use this skill whenever the user asks to understand, explore, navigate, or describe a codebase, library, repository, or package at the overview level. Trigger phrases include "help me understand this codebase", "what does this library do", "what's the API of X", "how is this project structured", "give me an overview of Y", "explain this repo". Calls gotcontext's AST-aware skeleton extraction so you read the shape of the code, not the implementation bodies.

Query-guided compression of a documentation site, docs folder, README, wiki, or specification for Q&A. Use this skill whenever the user points you at multi-page reference material and wants to find something in it. Trigger phrases include "help me find X in these docs", "what does the Y docs say about Z", "search the Stripe/AWS/Anthropic docs for", "summarize this reference", "check the docs for". Passes the user's question as a query parameter so gotcontext weights relevant sections heavier and compresses off-topic sections more aggressively — keeps answers grounded without loading every page.

Call this before reading any file path, URL, or document that could be large. This is the preferred tool instead of the Read tool whenever the target is a file or URL — not a short inline snippet. Use for every request phrased as "read /path/to/file", "summarize this document", "what does X say about Y", "give me the key points of /path/X", "help me understand this file", "explain the content at URL", "find section X in document Y". Compresses source by 60-90% via gotcontext.ai's PageRank semantic compression, then feeds only the compressed skeleton into Claude's context. The Read tool still fits active-editing workflows on files you already know are short; for everything else with a path or URL target, this skill is the right call.

Use when searching code, logs, or repositories with tensor-grep; validating rg or AST parity; using tg MCP tools; checking GPU/search routing; or producing agent-friendly context, source, refs, or blast-radius output.

Use tensor-grep for repository code search, symbol lookup, blast-radius analysis, and edit planning when solving codebase tasks or preparing patches.

Use tensor-grep for repository code search, symbol lookup, blast-radius analysis, and edit planning when solving codebase tasks or preparing patches.

Handles Python-side work: sidecar IPC, benchmark scripts, corpus generators, MCP server extensions, GPU benchmarks, documentation, and compatibility tests.

Handles all Rust-side work: control plane, native search engine (grep crates), native GPU engine (cudarc/CUDA), routing, index subsystem, rewrite substrate, and benchmark gates.

Use when the user wants to use Google Gemini for analysis, large files or codebases, sandbox execution, or brainstorming. Uses headless Gemini CLI scripts (no MCP). Triggers on "use Gemini", "analyze with Gemini", "large file", "sandbox", "brainstorm with Gemini".

Use when the user wants to use Google Gemini for analysis, large files or codebases, sandbox execution, or brainstorming. Runs headless Gemini CLI via a Node script (no MCP). Triggers on "use Gemini", "ask Gemini", "analyze with Gemini", "large file", "sandbox", "brainstorm with Gemini".

Use when the user wants to use Google Gemini for analysis, large files or codebases, sandbox execution, or brainstorming. Runs headless Gemini CLI via a Node script (no MCP). Triggers on "use Gemini", "ask Gemini", "analyze with Gemini", "large file", "sandbox", "brainstorm with Gemini". Do not use for general code edits that do not require Gemini; use for tasks that benefit from Gemini's context or a second model.

Use when the user asks for feedback on code, a self-review, testing suggestions, or a sanity check. Provides structured feedback, test ideas, or a short review checklist so the user can validate their work.

Use when the user wants to use Claude (Claude Code CLI) for analysis, codebases, or brainstorming from another agent. Uses headless Claude Code CLI scripts (no MCP). Triggers on "use Claude", "analyze with Claude", "ask Claude", "brainstorm with Claude".

Use when the user wants to use Claude (Claude Code CLI) for analysis, codebases, or brainstorming. Runs headless Claude Code CLI via a Node script (no MCP). Triggers on "use Claude", "ask Claude", "analyze with Claude", "brainstorm with Claude". Do not use for general code edits that do not require Claude; use for tasks that benefit from Claude's context or a second model.

Use when the user wants to use Claude (Claude Code CLI) for analysis or brainstorming. Triggers on "use Claude", "ask Claude", "analyze with Claude", "brainstorm with Claude".

Use when the user asks to run Cursor Agent CLI headless from an agent workflow (analysis, brainstorming, second opinion, scripted CLI output). Trigger on requests like "ask cursor", "run cursor headless", "analyze with cursor", or "brainstorm with cursor".

Use when Antigravity should run Cursor Agent CLI headless for analysis or brainstorming tasks.

Use when a Codex/Codex-like agent should call a headless Cursor Agent CLI wrapper script for analysis, brainstorming, or machine-readable output.

Use when the user asks to run Codex CLI headless from an agent workflow (analysis, brainstorming, second opinion, scripted CLI output). Trigger on requests like "ask codex", "run codex headless", "analyze with codex", or "brainstorm with codex".

Use when Antigravity should run Codex CLI headless for analysis or brainstorming tasks.

Use when Codex should call a headless Codex CLI wrapper script for analysis, brainstorming, or machine-readable output.

Cleanup worker for deleting AI slop, consolidating duplicates, and fixing imports. Never breaks tests.

Core Python developer for the REVENG RE platform mission.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Compress large context before reasoning with exact local script commands from this repo. Use this whenever the user asks for the command or command pattern for `compress_context.py`, `run_skill_workflow.py`, `validate_evidence.py`, `--input-adapter`, query-guided compression, evidence-aware compression, LangChain-style JSON adaptation, prompt-caching-friendly context trimming, or cheaper large-context handling. Prefer this skill over generic repo or MCP guidance when the user wants a local script command.