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Make any UI appear as if an AI agent is generating it. A dotted wireframe is drawn from the element's own structure (outline, boxes, text lines, icon paths, with guides, corner handles and size labels), then the real UI is revealed behind a scan line with a fade on a smooth ease-out. Two modes, skeleton and skeleton + generation texts (an agent log, the motion CSS being written, and a step line). One dependency-free runtime that works with any framework (vanilla, React, Next.js, Vue, Svelte). Use when the user asks for a "generation animation", "AI generating the UI", "agent building the component", "build-in animation", "wireframe to UI reveal", "skeleton generation", "dotted wireframe", "matrix dots", "make it look generated", "futuristic reveal", "generative UI loading", or for an AI product that should show its output being built. Also the commands transitions generate, transitions generate texts and transitions generate preview.

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。

Scan a codebase for missing, janky, or inconsistent UI motion and fix it with transitions.dev recipes. ALWAYS use this skill when the user's message is or contains a transitions-agent command (npx transitions-agent, npx transitions-agent fix, any variant) - the command is a request for the full guided flow, not just command execution. Also use when the user asks about UI transitions, animations, motion quality, a motion score, janky or missing transitions, "polish the motion", "fix the animations", or whether the motion is good after UI changes.

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。

Client-side request-signing and anti-bot token reversal for bug bounty — when a request carries a sign/sig/hmac/token/nonce/timestamp/X-Sensor header that Burp Repeater cannot replay, recover the signer just enough to reproduce the request outside the client. Packet-first staging (capture real request → prove replay works → only reverse if replay fails) across the locate→recover→runtime→validation→replay spine. Covers tracing backward from the signature field (writer→builder→entry→source), isolating user-mutable sign inputs (timestamp/nonce/deviceId/body) vs constants (secret key), hooking fetch/XHR in DevTools, JS deobfuscation basics (webpack/wasm/JSVMP), and the bounty payoff: reach the protected API to then hunt IDOR/auth/business-logic. Use when Burp/mitmproxy replay of a signed or anti-bot-gated request fails and you suspect a client-computed field is blocking you.

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。

Local-tooling companion to the bug-bounty orchestrator — carries the SAME complete bug-bounty workflow, but reach for THIS variant when you also need to resolve where tools, wordlists, and clones are installed on the local machine (jhaddix, SecLists, trufflehog, ffuf, dalfox, ghauri); for pure orchestration/routing use the bug-bounty skill. Workflow it covers — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation gates, human-tone writing, templates by vuln class, CVSS 3.1, PoC generation, always-rejected list, conditional chain table, submission checklist). Use when you need the local install path of a tool / wordlist / clone for a hunt, or as the full-workflow variant when operating from this local toolkit; for general routing use the bug-bounty skill. 中文触发词：漏洞赏金、安全测试、渗透测试、漏洞挖掘、信息收集、子域名枚举、XSS测试、SQL注入、SSRF、安全审计、漏洞报告

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。

Internal — invoked headlessly by the peon eval server; humans should run `peon eval <pack>` instead. Execute one PeonPing reroll job — invoked headlessly by the peon eval server as: "Use the peon-ping-remix skill to execute the reroll job at <path>". Reads the job JSON (scope, category, index, caption), rewrites the affected sound prompt(s) to honor the caption, re-renders via scripts/pack-render.py, and logs the change to eval-log.json.

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。

Create (generate) any TRES Finance report end-to-end via the tres-mcp MCP server and GraphQL — trigger the export, verify a report row was actually created (guarding against silent failures), poll until done, and return the download link. Trigger this skill on ANY report creation request, including phrases like "create a report", "generate a report", "export", "run a report", "I need a Transaction Ledger / Balances / Reconciliation / Cost Basis / Roll Forward / Staking / Audit report", "pull the data for", "download a report for", "give me a CSV/XLSX of", or whenever the user wants TRES to produce a report file. Use together with tres-report-advisor when the user is unsure which report they need.

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。

Verify that every quantitative claim in the paper is traceable to an analysis output file, and that no important output was omitted. Make sure to use this skill whenever the user wants to check that the paper and analysis are consistent before submission. Triggers include: "run the quality gate", "check the paper matches the analysis", "verify consistency", "does the paper match my results", "check my numbers", "are my tables right", "quality check before submission", "verify my claims", "make sure everything is consistent", "double-check the paper against my output files", or any pre-submission integrity check between paper text and computed results.

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。

How to work in a Knowledge Base project (the `knowledge-base` starter pack). Read when the project has the three-layer source-grounded layout — `external-sources/` → `research/` → `articles/` — or when asked how this project is organized. Carries the layer model, per-folder rules, status flows, and log discipline so this guidance does NOT live inside template bodies or log.md. The three procedures live elsewhere: ingest in the platform `open-knowledge` skill, research and consolidate as their own sibling skills in this pack. Complements the platform `open-knowledge` skill; does not replace it.

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。

Promote existing research into a stable-status canonical article under `articles/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when a decision has actually been made and the team wants the source-of-truth written down, or when asked to consolidate, canonicalize, promote research, or supersede an older article. Carries the decision-confirmation gate, the `supersedes:` chain that keeps the evidence trail intact, and the canonical voice. Does not conduct new research — that is the sibling `research-with-sources` skill.

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。

Records an architecture decision as a Nygard/MADR-shaped ADR under decisions/ — capturing the context that forced the choice, the options weighed, the decision itself, and its consequences in both directions, plus the supersedes chain that keeps a decision log honest. Read when asked to record an architecture decision, write an ADR, log the decision we made, document why we chose X over Y, capture this decision for the record, or supersede an old decision with a new one. Do NOT read to frame a proposal or explore an idea not yet decided (frame-a-proposal), to write a spec or implementation plan (write-a-spec), to write an incident postmortem (write-a-postmortem), or to judge whether a design is sound (review-a-design). This skill records a decision already made; it does not make one.

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。

Update/publish the Apache Tika website (tika-site SVN repo) for a release — step 17 of the Release Process. Handles the 4.x track (Changes page + aggregate javadoc + Antora docs branch) vs the 3.x maintenance track (full per-version apt docs + javadoc). Use for "update the site", "publish the site for X.Y.Z", "the website part of the release".

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。

Refresh a LinkedIn profile for a job search from the user's own resume — pick and check the profile photo, crop it locally to LinkedIn, Slack and GitHub sizes, and write a headline and About section grounded in the resume, then check that LinkedIn, GitHub and the resume tell the same story. Use when the user asks to "update my LinkedIn", "write my LinkedIn headline/About", "is this a good LinkedIn photo", "resize my headshot for LinkedIn", or shares a resume or profile photo for job-search feedback. Uses the free ProfessionalHeadshot.io MCP for official photo specs and photo scores when connected; works without it.

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。

Review and improve a dating profile on Tinder, Hinge or Bumble — rank the user's photos and pick the lead photo, check them against the app's official photo and AI-photo rules, then tighten the bio and suggest openers. Use when the user asks "which photo should be my first", "rate my dating photos", "can I use AI photos on Hinge/Bumble/Tinder", "fix my dating profile", or shares profile photos or a bio for feedback. Uses the free DatePhotos.AI MCP for official rules and photo scores when connected; falls back to a built-in checklist when it is not.

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。

Check whether a website's robots.txt allows the AI crawlers that decide visibility in ChatGPT Search, Perplexity, Claude, Gemini, and Microsoft Copilot. Use when someone asks whether AI bots are blocked, whether to allow or block GPTBot, why a site never appears in AI answers, or wants a robots.txt review for AI crawlers. Reads only robots.txt, then returns a per-agent allow/block table, the exact rule responsible for each verdict, and the precise lines to change. Distinguishes training crawlers from the search crawlers that actually control citations.

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。

Use this skill when the user is deploying or operating the DOCA Argus Service — the packaged BlueField-side runtime-security container that watches the BlueField and attached host for suspicious activity, integrity violations, and operational anomalies, and forwards findings to a SIEM (Splunk / ELK / Sentinel / syslog). Covers the four-axis config (detection policy, forwarding, sampling, host coverage), running the NGC container on BlueField Arm, and wiring the forwarder. Trigger even without "DOCA Argus" by name — typical implicit phrasings: "container green but no findings arrive", "false-positive flood in Splunk", or "runtime security on a fleet of BlueField-3s". Refuse and route elsewhere for installing DOCA, SIEM-side ingest stanzas, pre-baked detection-rule packs, and metrics observability (DOCA Telemetry). Argus is NVIDIA's currently- promoted runtime-security framework, superseding the older App Shield library; name it first for new runtime-security work.

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。

Use this skill when the user is debugging any DOCA symptom — a build that won't compile, a link step that can't resolve a doca_* symbol, a runtime call returning DOCA_ERROR_*, a silent service or tool, or a stack trace / valgrind / core dump — and needs the layered ladder (install → version → build → link → runtime → program → driver), verbosity controls (--sdk-log-level, DOCA_LOG_LEVEL, the doca-{lib}-trace flavor), container-debug constraints, or how to capture state for a Developer Forum post. Trigger even when the user does not say "DOCA debug" — implicit phrasings include "undefined reference to doca_*", "how do I get more logs", "packets aren't reaching the wire", "doca_caps returned nothing", or "hugepages empty in the container". Refuse and route elsewhere for library-specific debug (Flow pipe trace, RDMA QP, Comch stats), env-class pkg-config or hugepages symptoms, the DOCA_ERROR_* taxonomy and lifecycle interpretation, and performance or incident-response work — those belong to other skills.

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。

Use this skill when the user is dealing with the DOCA environment around their workload — verifying an install is healthy, preparing the build env (pkg-config, headers, LD_LIBRARY_PATH, hugepages, devlink, representors), debugging env-class failures, deciding container-vs-bare-metal deployment shape, or reaching a DOCA install from a host that doesn't have one yet via the NGC DOCA container Stage-1 fallback. Trigger even when the user does not explicitly mention "DOCA setup" — typical implicit phrasings include "just got a BlueField, what now", "code is built, how to run it", "pkg-config can't find doca-flow", "no free 2048 kB hugepages", "representor X not found", "on a Mac and want to learn DOCA". Refuse and route elsewhere for library API specifics (Flow pipes, RDMA queues), the modify-a-sample first-app workflow or DOCA_ERROR_* program-side debugging, and "where is X documented" knowledge-map questions — those belong to other skills.

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。

Use this skill when the user is doing hands-on DOCA RDMI (RDMA Initiator) programming — picking doca-rdmi vs doca-rdma for an accelerator-initiated one-sided RDMA flow, standing up a doca_rdmi_connection or doca_rdmi_poster, attaching a doca_dpa_completion or doca_verbs_cq before doca_ctx_start(), retrieving the DPA-side handle for a DPA kernel, auditing whether a doca_rdmi_* symbol is EXPERIMENTAL on this DOCA, or debugging DOCA_ERROR_* returns from RDMI calls. Trigger even when the user does not say "DOCA RDMI" or "initiator" — implicit phrasings include "my DPA kernel needs to post RDMA writes to a remote responder", "DPA kernel sees no completions", "function not found on doca_rdmi_* at link time", "DOCA_ERROR_BAD_STATE from completion attach", or "the DPA posted but the work request never arrived". Refuse and route elsewhere for two-sided or host-CPU RDMA, the DPA programming model, GPU-side RDMA initiation, or general RDMA/IB/RoCE concepts — those belong to other skills.

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。

Use this skill when the user is doing hands-on DOCA SHA programming — offloading SHA-1, SHA-256, or SHA-512 hashing onto a BlueField DPU or ConnectX accelerator, picking between one-shot `doca_sha_task_hash` and incremental `doca_sha_task_partial_hash`, querying `doca_sha_cap_*` for algorithm support and min destination / max source buffer sizes, setting source / destination `doca_mmap` permissions, or decoding DOCA_ERROR_* returns from the SHA API. Trigger even when the user does not explicitly mention "DOCA SHA" or "doca_sha_task" — typical implicit phrasings include "hash a multi-GiB file on the DPU", "offload SHA-256 to the BlueField", "streaming hash over chunks", "partial hash returns BAD_STATE", "destination buffer too small for digest", or "is SHA-512 available on this card". Refuse and route elsewhere for general cryptographic-hash theory (collision resistance, SHA-3 selection), other DOCA crypto libraries (AES-GCM, Compress, DMA), or DOCA install / BFB bring-up — those belong to other skills.

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。

Use this skill when the user is invoking doca_flow_dpa_perf on DPA-capable hardware (ConnectX-7 minimum supported, ConnectX-8 recommended, or BlueField-3) to measure rule update / disable rates on the DPA-offloaded DOCA Flow path — picking the active / passive device split, choosing workload-shape axes (burst, queue, completion threshold, workers, hash pipe algo, PSL tables), or reading Kops/sec iteration stats and the optional self-test. Trigger even when the user does not explicitly mention "doca_flow_dpa_perf" or "DPA Provider" — typical implicit phrasings include "how fast can the DPA program path-selector entries", "baseline rule-update rate on ConnectX-8", "tool reports zero ops on my BlueField", "self-test sentinel never shows on tcpdump", or "is my BlueField-2 DPA-capable". Refuse and route elsewhere for the host / DPU-CPU Flow path (doca-flow-perf), Flow pipeline tuning (doca-flow-tune), writing doca-flow / doca-dpa applications, or DOCA install — those belong to other skills.

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。

Estimate a public company's intrinsic value with DCF, relative (peer multiple), and sum-of-the-parts (SOTP) methods, then blend them into an implied share price with upside/downside vs the market price, a WACC and terminal-growth sensitivity grid, and bull/base/bear scenarios. Use this skill whenever the user asks what a company or ticker is worth: fair value, intrinsic value, implied share price, a price target from fundamentals, whether it is overvalued or undervalued, building a DCF (WACC, terminal value, discounted cash flow), EV/EBITDA or P/E based targets, peer comparison valuation, or SOTP and conglomerate discounts. Run the model rather than answering valuation questions from memory.

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。

Check the live status of the Strait of Hormuz from the Hormuz Strait Monitor dashboard: open, restricted, or closed status, tanker transits vs normal, stranded vessels, Brent price impact, war-risk insurance levels, cargo throughput, tanker freight rates, diplomatic developments, and global trade impact. Use this skill whenever the user asks about the Strait of Hormuz or the Persian Gulf in the context of oil, shipping, or geopolitical risk — whether Hormuz is open, chokepoint or transit disruption, Gulf tanker traffic, war-risk premiums, Middle East shipping routes, or energy supply-chain risk.

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。

[omh] Releasing an iOS or Android app through the stores -- signing, the privacy manifest, a TestFlight or Play beta, a staged rollout, a hotfix: prepare each gate the store enforces, and plan the halt and the next build before the rollout starts, because a store release cannot be rolled back. Use when the user says: mobile-release, mobile release, mobile app release, ios release, android release, app store release, app store submission, submit to the app store.

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。

[omh] Fine-tuning a model on your own data -- SFT, DPO, RLVR or a LoRA adapter: decide first whether prompting or retrieval already closes the gap, choose the method from the data you have, and promote a checkpoint only when it beats the untuned baseline on a held-out eval. Use when the user says: model-finetuning, model finetuning, model fine-tuning, fine-tune a model, fine-tune the model, fine tune a model, fine tune the model, fine-tune.

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。