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概要と使いどころ
audiowaveform is a BBC open-source C++ CLI tool that generates waveform data from MP3, WAV, FLAC, Ogg Vorbis, and Opus audio files. It outputs binary or JSON waveform data and renders PNG waveform images at configurable zoom levels.
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Separates audio tracks into individual stems (vocals, drums, bass, other) using Meta's Demucs neural network model via the demucs Python package. Supports batch processing of WAV and MP3 files, outputs isolated stems in FLAC or WAV format, and integrates with FFmpeg for format conversion and loudness matching post-separation.
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Apify SDK is the official JavaScript SDK for building Actors, crawlers, and data extraction workflows on Apify. It gives agents a structured way to run scraping jobs, store outputs, manage inputs, and combine crawler logic with browser automation when needed.
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Extracts structured text, metadata, and embedded objects from PDFs, Office documents, and 1000+ file formats using the Apache Tika REST API. Outputs clean Markdown or JSON with XMP metadata preservation.
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Wraps Apache Tika Server REST API for extracting structured text from PDFs, DOCX, PPTX, and 1,200+ file formats. Outputs clean markdown with metadata preservation using Tika /rmeta/text endpoint and recursive parsing mode.
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Bulk-generates SEO meta descriptions from page content using OpenAI's GPT-4o API with custom system prompts tuned for SERP CTR optimization. Validates character limits, checks for duplicate descriptions via Screaming Frog CSV exports, and outputs Yoast SEO-compatible import files.
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Calls Adobe Firefly's text-to-image and generative fill APIs for batch asset creation. Manages Adobe IMS OAuth tokens and enforces Content Credentials (C2PA) metadata on all outputs.
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Analyzes a project description and outputs a structured bid: scope, effort, tech risks, and optional pricing.
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Use Tokenless for large files and noisy tool outputs before they enter context. Must be used when commands, Read output, diffs, logs, or search results are large.
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Use when imp outputs differ between two paths that should agree - chunk sizes, prefill vs decode, prefix-cache resend, two engines (imp vs llama.cpp vs HF), two builds or kernels - "output changes with chunk size", "first token differs", "logprobs moved", "which layer diverges", "who is right, imp or llama.cpp", "near-tie". Do NOT use for repetition loops or garbage output (check-degeneration), a new arch that loads wrong (add-model-arch), or perf (benchmark-cuda).
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Use when adding support for a new model architecture to imp, porting a model family, or debugging a model that loads but produces wrong output - "add support for <model>", "new arch", loader detection, chat template, tokenizer parity, RoPE variant, "outputs garbage", "prompt-blind", "digits scrambled", "NaN logits", "describes a different picture", "does it fit in VRAM". Do NOT use for kernel performance (sm120-cuda-expert) or quant-format questions (quant-formats).
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Verify that task outputs actually achieve stated goals through 4 progressive levels of verification
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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Generate a complete subcontract from a firm's template, awarded bid, and project specs. Outputs a Word document. Triggers: 'subcontract', 'sub agreement', 'write contract', 'draft subcontract'. Requires awarded bid.
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Phase 1 of 5 — Socratic interview capturing WHAT and WHY; outputs a sprint-sized REQ doc for plan-architecture. Use only when the user asks to run Phase 1, plan requirements, or start the requirements interview — never trigger automatically from a feature description.
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Phase 2 of 5 — collaborative system design plus change-footprint mapping; outputs an ARCH doc for generate-tasks. Use only when the user asks to run Phase 2, plan the architecture, or design the solution — never trigger automatically from a design question.
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Textum PRD→Scaffold→Story workflow for Codex with low-noise outputs and gate checks.
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Systematically gather comprehensive travel itinerary requirements through structured discovery questions, MCP-powered research (Perplexity, Exa), and expert detail gathering. Use when users request travel planning, itinerary creation, or trip assistance requiring deep research and personalized requirements gathering. Outputs detailed requirements specification with day-by-day itineraries.
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Use this skill when tool outputs exceed 2000 tokens, tasks span multiple conversation turns, sub-agents need to share state, context window is bloating, plans need to persist across summarization, terminal/log output needs selective querying, or when user mentions "offload context", "dynamic context discovery", "filesystem memory", "scratch pad", "reduce context bloat", or "just-in-time context loading".
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AI internal coordination using stigmergic signals — managing information freshness in context and memory, decay rates for assumption staleness, and emergent coherent behavior from simple local protocols. Use during complex tasks where multiple sub-tasks must coordinate, when context has grown long and information freshness is uncertain, after context compression when information may have been lost, or when sub-task outputs need to feed into each other cleanly without degradation.
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Operator-side inventory of experiment PRs for auditing a Senpai research track. It outputs merged winners, a compact results table, and full details for deep dives.
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Use this skill when the user wants to create, maintain, query, or visualize a local Markdown knowledge base. ThinkWiki can initialize a wiki, import files or webpages, collect inbox items, answer from existing knowledge, save higher-value outputs, and generate HTML viewer and graph artifacts.
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Save, organize, and describe reusable analysis artifacts such as SQL, result snapshots, CSV exports, summaries, caveats, plots, and report-ready files. Use when users ask to save, export, share, cite, reproduce, or organize data-analysis outputs.
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Solve competition math problems (IMO, Putnam, USAMO, AIME) with adversarial verification that catches the errors self-verification misses. Activates when asked to 'solve this IMO problem', 'prove this olympiad inequality', 'verify this competition proof', 'find a counterexample', 'is this proof correct', or for any problem with 'IMO', 'Putnam', 'USAMO', 'olympiad', or 'competition math' in it. Uses pure reasoning (no tools) — then a fresh-context adversarial verifier attacks the proof using specific failure patterns, not generic 'check logic'. Outputs calibrated confidence — will say 'no confident solution' rather than bluff. If LaTeX is available, produces a clean PDF after verification passes.
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