Ar9av

Ingest documents into the Obsidian wiki by distilling their knowledge into interconnected wiki pages. Use this skill whenever the user wants to add new sources to their wiki, process a document or directory, import articles, papers, or notes into their knowledge base, or says things like "add this to the wiki", "process these docs", "ingest this folder". Also triggers when the user drops a file and wants it incorporated into their existing knowledge base. Also handles raw mode: "process my drafts", "promote my raw pages", or any reference to the _raw/ staging directory.

Export the Obsidian wiki's knowledge graph to structured formats for use in external tools. Use this skill when the user says "export wiki", "export graph", "export to JSON", "export to Gephi", "export to Neo4j", "graphml", "visualize wiki", "knowledge graph export", or wants to use their wiki data in another tool. Outputs graph.json, graph.graphml, cypher.txt (Neo4j), and graph.html (interactive browser visualization) into a wiki-export/ directory at the vault root.

Import a wiki knowledge graph from a graph.json export file into the current vault. Use this skill when the user says "import wiki", "import from export", "load graph.json", "import vault", "/wiki-import", or wants to transfer pages from one vault to another using the output of wiki-export.

Fast, zero-friction capture of technical findings from the current conversation to the wiki's _raw/ staging area. Use this skill when the user says "/wiki-quick-chat-capture", "quick capture", "capture this finding", "save this bug fix", "capture this gotcha", "drop this to raw", "quick save to wiki", or wants to capture a non-obvious discovery mid-session without a full wiki-ingest run. Writes one _raw/ file per topic cluster in under 60 seconds — no subagents, no QMD updates, no manifest writes. Run /wiki-ingest or /data-ingest later to promote raw files to proper wiki pages.

Sync the current project's knowledge into the Obsidian wiki. Use this skill from any project when the user says "update wiki", "sync to wiki", "save this to my wiki", "update obsidian", or wants to distill what they've been working on into their knowledge base. This is the cross-project skill that lets you push knowledge from wherever you are into the vault.

Browse and compare wiki knowledge by which AI tool originally produced it. Use this skill when the user says "/memory-bridge", "browse codex memory", "what did codex know about X", "show me claude knowledge", "cross-tool memory", "what does hermes know that claude doesn't", "show me knowledge from <tool>", "compare my AI tool memories", or wants to explore knowledge gaps between tools. Works from any project. Diff mode ("what's different", "unique to codex", "gaps between tools") is the killer feature — it surfaces blind spots between tools that the user may not know exist.

Ingest Pi coding agent session history into the Obsidian wiki. Use this skill when the user wants to mine their past Pi sessions for knowledge, import their ~/.pi/agent/sessions folder, extract insights from previous coding sessions, or says things like "process my Pi history", "add my Pi sessions to the wiki", "ingest ~/.pi", or "what have I worked on in Pi". Also triggers when the user mentions Pi sessions, Pi agent history, ~/.pi/agent/sessions, or Pi conversation logs.

Query-driven targeted ingest from a specific AI agent's raw history. Use this skill when the user invokes /wiki-claude, /wiki-codex, /wiki-hermes, /wiki-openclaw, /wiki-copilot, /wiki-pi — with or without a search topic. Different from wiki-history-ingest (which bulk-ingests everything new): this skill finds sessions about a SPECIFIC TOPIC in a specific agent's history and ingests just those, then returns a synthesized answer immediately usable in the current session. Primary use case: you're working in agent A and want to pull in how you solved X in agent B's history. Cross-referencing, not archiving. Also trigger on: "what did I work on in codex about X", "search my claude sessions for Y", "pull in hermes knowledge about Z", "find that conversation where I did X in codex".

Generates genre-appropriate sound effects and looping background music for a generated game. When ELEVENLABS_API_KEY is set, calls the ElevenLabs text-to-sound-effects API for real MP3 audio; otherwise falls back to Web Audio oscillator synthesis. Produces sfx.json, music.json, and AudioManager.js. Run in parallel with art generation stages.

Writes the gameplay code for a Phaser 3 game (src/scenes/Game.js plus optional helpers). Reads the GDD, levels, and sprite/tile manifest; produces ES module code that consumes assets by name (never magic numbers). Use after sprite-artist and tile-artist have produced a complete manifest.

Orchestrator skill that turns a one-line description into a runnable Phaser 3 game. Coordinates eight sub-skills (game-designer, world-architect, sprite-artist, tile-artist, bg-artist, codesmith, playtester, refiner) into a deterministic-where-possible, LLM-where-necessary pipeline. Image generation uses GPT Image 2 (gpt-image-2) at low quality by default. Use when the user wants to generate, refine, or test a 2D HTML5 game from natural language.

Static analysis of Game.js that cross-references every animation key and texture key against manifest.json. Catches "Animation not found" and "Texture not found" bugs before the browser is opened. Run after codesmith, before playtester.

Checks 5 mobile-compatibility properties in the generated game (roundPixels, image-rendering CSS, devicePixelRatio, touch input, AudioContext unlock) and patches any missing items. Run after codesmith, before playtester.

Quantizes all generated PNG assets (sprites, tiles, background) to a named 8-bit color palette to achieve visual coherence. Run after sprite-artist, tile-artist, and bg-artist, before codesmith. Writes state.style.palette so downstream skills know the chosen palette.

Replaces the serial playtester → gap-checker chain with 4 concurrent QA workers: static analysis (no browser), golden-path scenario runner, dynamic fuzzer, and physics debug validator. Merges all results into a single failure list for the refiner. Cuts QA time from ~2 min to ~30s.

Boots the generated game in headless Chromium with physics debug mode enabled, captures screenshots of all physics bodies, and emits a structured report of hitbox positions vs sprite positions. Use this to catch invisible hitbox misalignment bugs that only appear visually. Run in parallel with the other QA checks.

Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to flag hallucinated citations, build a BibTeX file, and draft Introduction + Related Work using ≥90% of the verified pool. Runs in parallel with the plotting-agent. TRIGGER when the orchestrator delegates Step 3 or when the user asks to "find citations for my paper", "draft the related work", or "build the bibliography".

Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper from my experiments", "turn this idea and these results into a paper", "generate a conference submission", "run paper-orchestra on X", or otherwise wants the end-to-end paper-writing pipeline. Coordinates the outline-agent, plotting-agent, literature-review-agent, section-writing-agent, and content-refinement-agent skills.

Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules. Maintains a worklog and snapshots each iteration so revert is real, not symbolic. TRIGGER when the orchestrator delegates Step 5 or when the user asks to "refine the draft", "iterate on the paper", or "run peer review on this paper".

Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.

Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimental_log.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER when the orchestrator delegates Step 2 or when the user asks to "generate the figures for my paper" or "render the plots from this experiment log".

Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges everything into the template that already contains Intro + Related Work from Step 3. TRIGGER when the orchestrator delegates Step 4 or when the user asks to "write the methodology and experiments sections" or "fill in the rest of the paper".

Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimental_log.md, template.tex, conference_guidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator delegates Step 1 or when the user asks to "outline a paper from raw materials" or "generate the paper structure".

Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the user asks to "score this paper draft", "evaluate against the benchmark", "compare two papers", or "run the autoraters".