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Fix RFDB V2 graph data silently not persisting to disk after analysis. Use when: (1) rfdb-server reports "0 nodes, 0 edges" on restart despite successful analysis, (2) segment directories exist but are empty (no .bin files), (3) manifest_index.json shows total_nodes: 0 despite analysis logging 70k+ nodes, (4) Docker builds produce empty graph databases, (5) --clear flag used before analyze command. Root cause: GraphEngineV2::clear() replaces the store with MultiShardStore::ephemeral() which has path: None, causing all flush operations to write to in-memory buffers only.

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Force `rust-embed` to re-embed assets from `$GRAFEMA_UI_DIST` (or any env-driven path) when cargo's incremental compilation skips the macro re-expansion. Use when: (1) you rebuilt the GUI bundle but the rust-server binary still serves the OLD bundle, (2) `cargo build --release` finishes in seconds (incremental hit) and `strings target/release/binary | grep <new-hash>` finds nothing, (3) any rust-embed pipeline where assets live outside the cargo source tree and are picked up via env var or symlink. Cargo only re-runs the proc macro when the rust file declaring `#[derive(RustEmbed)]` changes — env var changes alone don't trigger it.

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Fix silent incorrect results in RFDB Datalog queries that share a variable between two edge atoms on opposite ends (e.g. self-join patterns like `edge(M, C, "T1"), edge(C, M, "T2")`). Use when: (1) a Datalog rule returns rows that look structurally wrong (e.g. a self-loop rule reports hits but pinning M to each reported id refutes the edge existence), (2) Cypher `MATCH (m)-[:R1]->(c)-[:R2]->(m)` returns the same bogus rows (engine-level bug, not Datalog-specific), (3) results change when you bind one variable explicitly vs. leave it free. Root cause: hash- join fast path in `eval_edge_hash_join` / `eval_incoming_hash_join` unconditionally rebinds the non-key Term::Var without checking whether the variable is already bound in the current row.

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RFDB server becomes unresponsive (60s+ timeouts) during enricher addEdges write storms. Use when: (1) RFDB CPU spikes to 60-90% after addEdges calls; (2) subsequent reads time out at 60s; (3) server stays catatonic for 5-15 min after a write storm; (4) Tokio main thread parked (write-lock contention). Root cause: add_edges() calls maybe_auto_flush() which has a memory-pressure path that fires on NODE count — not edge count — and holds the exclusive write lock during disk I/O. Fix: separate maybe_auto_flush_edges() uses byte-limit only.

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Diagnose React + Three.js (or any browser canvas) frame stutter by capturing a CDP CPU profile via Playwright instead of guessing from `[perf]` console logs. Use when: (1) frame time is bad but `[perf]` instrumentation doesn't pinpoint the offender, (2) you've spent more than ~30 minutes "fixing" suspects without measuring, (3) hot paths involve animation systems, allocation bursts, or invisible scene-graph traversal, (4) you need self-time per function with line numbers, not aggregate "render slow". The `console.warn` pattern is good for monitoring; CDP is needed for diagnosis. Covers script template, sourcemap setup so minified function names are decoded, and interactive variant where the user drives the interaction and signals stop via `touch /tmp/<flag>`.

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Fix silent failures when parsing Grafema semantic IDs that are in URI format instead of legacy arrow format. Use when: (1) code splits semantic IDs by "->" but gets the whole string back because IDs are grafema:// URIs, (2) file path extraction from semantic IDs returns empty or wrong values, (3) derived edges (DEPENDS_ON, etc.) produce 0 results despite source edges existing, (4) any code that processes semantic IDs after the analysis pipeline's to_uri_format() has run. The grafema:// URI format uses # fragments with percent-encoded characters instead of -> separators.

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Grafema release procedure for publishing new versions to npm. Covers happy path, pitfalls, rfdb binary lifecycle, and rollback. Use when user says "release", "publish", "bump version".

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This skill should be used when the user asks to "make a whiteboard animation", "draw-on explainer", "VideoScribe style", "hand-drawn explainer video", "animate a sketch being drawn", "do a doodle/scribe video", "show a hand drawing the illustration", or "animate handwriting appearing on a whiteboard". Covers SVG stroke draw-on, a marker hand that follows the path tip, staggered reveal, erase/wipe transitions, and narrated export.

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Execute the fast path for trivial changes with minimal governance ceremony. Use when: (1) User invokes /quick, (2) Change is doc-only or non-behavioral, (3) No RFC or ADR work is needed

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Use for ANY bug bounty, penetration test, or web/API/mobile security assessment: recon, testing endpoints, analyzing Burp traffic or any bug-bounty platform's MCP (HackerOne, Intigriti, Bugcrowd, YesWeHack, Immunefi), reviewing APKs, bypassing a WAF, enforcing scope, hunting a specific vuln class, validating findings, writing reports, or when you are stuck on a target and about to give up. Activates automatically for all bug-bounty and pentest sessions. Runs an autonomous, never-surrender predator methodology: refuses to conclude "secure", exhausts each vuln class to depth, proves a real attacker-path with impact and no false positives, tracks coverage so nothing is re-tested or skipped, diagnoses failed tooling from full logs, and hunts at maximum effort with intelligent token use until the goal is found. Activates and follows instructions in ANY language the operator writes in (the skill itself is English). Bundles a per-class web-exploitation KB, a writeup library, and PayloadsAllTheThings.

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Before any material decision — code chunk, plan, strategy, design, scope cut, push to GitHub — convene a war-room. The session driver takes the CEO seat: frame the tradeoff, summon specialist seats from whatever agent roster the user has built, dispatch a cross-family voice via `ato dispatch` so priors actually disagree, decide. A failure-mode filter (wrong assumptions / overcomplexity / orthogonal edits / imperative-over-declarative — Karpathy's four are one good default, swap in your own) runs on every dispatch. Place in the v2.16 stack: war-rooms DECIDE before code starts; `ato-mission` EXECUTES the work between decisions (multi-step, goal-driven, persisted across days); `ato-review` VERIFIES the resulting commits. Use a war-room for the design verdict, hand the verdict to a Mission, review the merged result. Fires before: sending a code draft to the user as final, opening a PR, pushing to a remote-tracking branch, committing >50 LOC of behavior change, or delivering a plan or strategic recommendation as the final answer.

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Generate personalized review responses for negative, neutral, and positive reviews — empathetic, professional, conversion-focused with platform-specific tone

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Reads, searches and counts Claude Code session transcripts (the .jsonl files under ~/.claude/projects, subagents too) with the csift command-line tool, and ships hooks that re-inject compacted turns or record pending questions. Use when asked what a past or running session said, did, edited, spent or is doing now; when about to grep, jq or script over a transcript jsonl (a type=="user" filter overcounts the person's messages), count how many messages the person typed, tool calls or tokens, or claim something was never done or decided; to rebuild a lost file or plan from what a session wrote, extract pasted images, restore turns a compaction summary lost, find a subagent's own id when CLAUDE_CODE_SESSION_ID names the parent, wait on or message another session or subagent; or when csift prints "unknown label selector", "show needs an address", "looks like a session/agent id, not a project path" or "0 matches — a DEFINITIVE absence". Not for semantic or embedding search: csift matches regular expressions only.

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Append a DiT-tuning round to history.yaml. DiT-side counterpart of the LLM-path accuracy_append.py; records practice.md5, inference_outputs, fp_baseline_outputs, and the scoring fields (scores / overall_score / loss_vs_baseline / is_satisfied) populated by quant-tuning-score-dit. Idempotent on practice_id.

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Install and verify the msmodeling development environment. Use when the user explicitly asks to install msmodeling dependencies, set up this repository, create `myenv` with `uv`, install this repository's `requirements.txt`, set project `PYTHONPATH`, or configure `HF_ENDPOINT`; if the user only says to install an environment, ask whether they mean msmodeling dependencies before proceeding.

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Find realistic link prospects with SERP and backlink evidence, discover contact paths via web tools, and draft personalized outreach — with targets saved to .seo/backlink-targets.json for off-page-seo campaigns. Use this skill when the user has a linkable asset (study, tool, guide, template, product page) and wants backlinks, resource-page inclusions, or comparison mentions. Triggers: "link prospecting", "find backlink opportunities", "resource page outreach", "get backlinks for", "link building outreach".

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Read tweets, threads, bookmarks, articles, and user timelines on X (formerly Twitter) using an authenticated GraphQL flow. Use this skill whenever the user mentions a tweet URL, a Twitter thread, an X bookmark, a Twitter article, reading their X bookmarks, pulling a thread, or grabbing X content — even if they don't explicitly say 'use mktg-x'. Requires MKTG_X_AUTH_TOKEN and MKTG_X_CT0 env vars. Do NOT firecrawl x.com URLs — Twitter serves a degraded logged-out page and firecrawl will return the login wall. mktg-x is the only path for auth-walled X content.

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当用户需要深入理解目标客户的心理动机来撰写有共鸣的文案时调用。 适用于：写文案前分析客户心理、客户画像不够深入、文案太平面缺乏情感共鸣、 做用户调研准备。核心信号：用户在问"客户到底在想什么""怎么打动他们"。 不适用于：纯功能型产品文案（技术规格就够了）、不需要情感共鸣的B2B技术文档。

日本語の概要は準備中です。原文の説明を表示しています。

Show recent Claude Code sessions across all projects, so the user can re-enter work after a reboot or context switch. Lists per session the absolute project path, a short summary, where the session stopped, last activity, turn count, git branch, model, and a ready-to-paste resume command; can search sessions by what was said in them, flag projects with uncommitted or unpushed work, and re-open every session at once in new terminal tabs. Use when the user asks "what was I working on", "which projects did I touch recently", "where did I leave off", "list my recent sessions", "how do I get back into that session", "open all my sessions again", "which session did I fix X in", "where do I still have uncommitted work", or invokes /recap. Reads only local files; the default run is instant and offline.

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TypeScript and JavaScript performance and code-quality rules, usable in any framework. Use when writing, reviewing, or refactoring hot paths, data-structure choices, async flows, or bundle-size-sensitive code. Triggers include "optimize this function", "too slow", "reduce allocations", "bundle size", "lazy load", "speed up imports", or general code review of utilities and services.

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Bulk extract content from an entire website or site section. Use this skill when the user wants to crawl a site, extract all pages from a docs section, bulk-scrape multiple pages following links, or says "crawl", "get all the pages", "extract everything under /docs", "bulk extract", or needs content from many pages on the same site. Handles depth limits, path filtering, and concurrent extraction.

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Efficiently extract and convert the contents of any local file—such as PDF, DOCX, DOC, ODT, RTF, XLSX, XLS, or HTML—into clean, well-formatted markdown saved to disk. Use this skill whenever the user requests to parse, read, or extract information from a file on their computer, including phrases like “parse this PDF”, “convert this document”, “read this file”, “extract text from”, or when a local file path (not a URL) is provided. This skill offers advanced options like generating AI-powered summaries and answering questions based on the file's content. Prefer this tool over `scrape` when handling local files to deliver precise, structured outputs for downstream tasks.

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Design and review autonomous or semi-autonomous agent loops — systems where an agent prompts itself, runs on a schedule or event, and iterates toward a goal without a human at the keyboard. Use this whenever the user wants to build, design, harden, or audit any kind of self-running agent, agentic workflow, control loop, background worker, cron/heartbeat/webhook-driven agent, "Ralph loop", auto-PR-fixer, monitoring agent, or any long-running task that should keep going on its own until done. Trigger this even when the user says "agentic workflow", "automation that runs itself", "an agent that keeps trying until X", or "make my agent stop running away / burning tokens" without using the word "loop". Also use it to review an existing loop's stopping conditions, guardrails, verification, and escalation paths.

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Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.

日本語の概要は準備中です。原文の説明を表示しています。