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10-team parallel deep analysis with cross-verification for any topic, repository, or technology. Use when user invokes /research or asks for comprehensive research.

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。

On explicit /homework invocation, analyze the current and linked previous sessions, extract mistakes (찐빠), and report them via omcustom-feedback with a confirmation gate. Auto-activation on session cleanup/session-end signals is OPT-IN (default OFF) — requires an explicit project/user directive. Use when explicitly auditing recent work for harness gaps.

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 the repository for hardcoded secrets, exposed API keys, insecure network configurations, and vulnerable dependencies. Use when the user asks for a security audit, secret detection, credential scan, vulnerability check, or wants to find leaked tokens in the codebase.

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。

Spin up a working LiveAvatar demo from a curated catalog — clones the reference repo, installs dependencies, provisions the LiveAvatar account, and fills the env file, then hands off the parts only a human can do. Use when: (1) User wants to try, test, see, or demo LiveAvatar, (2) User asks what LiveAvatar demos or examples exist, (3) User wants a sales agent / lead-qualification avatar, (4) User wants a LiveKit agent driving a LiveAvatar avatar, (5) User wants an avatar joining their own LiveKit room, (6) User wants a speech-to-speech avatar, a GPT-Live demo, a language-learning or tutor avatar, a customer-support avatar, a poker coach / game-style avatar, or an avatar with on-screen visuals/overlays, (7) User says "run the LiveAvatar demo", "show me LiveAvatar", "get me started with LiveAvatar", or "clone the LiveAvatar example", (8) User is evaluating LiveAvatar and wants something running before writing their own integration.

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。

Task-sized forensic analysis of Vivado synthesis and routed timing evidence for processor RTL. Use when Codex must trace a named setup or hold path, audit whole-design timing populations, compare implementation runs, map primitive, LUT, CARRY, BRAM, DSP, MUXF, register, and routed-net stages back to Chisel or generated RTL, distinguish logic from routing pressure, identify missing DCP queries, or write an evidence-backed timing report. This skill is read-only by default and stops at ranked modification directions. Use optimize-chisel-fpga-timing for RTL edits and routed A/B implementation closure.

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。

Audit an existing UI for usability, hierarchy, accessibility, and conversion problems. Reviews a live URL, screenshot, or codebase against heuristics and reports prioritized issues with fixes. Use when the user says "audit this UI", "what's wrong with this screen", "UX review", "find the problems", "why is this confusing", or has existing interface to critique. Part of the Product Design set. Can drive a browser for live audits.

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 a prototype against its original design direction and requirements before shipping. Compares implementation to the mockup/spec/context, flags drift in layout, spacing, type, color, states, and behavior, and reports a pass/fix list. Use when the user says "design QA", "does it match the design", "check against the spec", "conformance check", or before sharing/handing off a prototype. Part of the Product Design set. Uses a browser tool for visual comparison.

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。

Run the project's own tests, linter, type checker and build, and report what actually failed.

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。

Bridge Codex to web-based AI model products by packaging local task context, scrub-checking it, sending it through an approved browser surface to ChatGPT Pro, Claude, Grok, Gemini, or another web model, waiting for the answer, and returning the model response to the user or Codex. Also guide DevSpace-like MCP Connector Mode when the user wants ChatGPT Pro or another MCP-capable web host to access approved local workspaces without relying on Codex browser automation. Use when the user asks for GPT Pro, ChatGPT Pro, Claude web, Grok, Gemini web, web model bridge, external model consult, ask another model, second opinion, use a web AI model, browserless agent access to GPT Pro, MCP connector, DevSpace-like workflow, or send local repo context to a browser-based model for planning, review, debugging, architecture discussion, or implementation guidance.

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。

Passive code-review lane for authorized repositories, local checkouts, uploaded source archives, and diffs. Use when the security research agent needs deterministic candidate discovery before expensive AI investigation, scoped PR-style review, append-only evidence records, or independent revalidation of code findings.

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。

Assess package choices and generated dependency names, publisher or release anomalies, incident exposure, and changes in package risk reports. Use before trusting a new dependency, when a release changes unexpectedly, or when investigating a supply-chain alert.

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。

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

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。

Record measured or directly seen security-research behavior as cited Observations, classify it against reusable security concepts, and decide whether it remains an observation, becomes a Lead, or supports a Finding. Use after passive analysis, a tool run, a probe, a crash, a timing anomaly, or a user-supplied behavioral report produces noteworthy evidence.

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。

Passive, pattern-calibrated risk discovery for authorized security research over an open-source project, repository URL, dependency, release, or local checkout. Use when the security research agent needs to find highest-risk areas, map attack surface, identify likely bug-hunting targets, prioritize security review, inspect structural/logical/security-critical code, compare vulnerable versus patched versions, reason from broad high-severity vulnerability families, or remember areas of interest for later attack-chain research.

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。

Investigate anonymous or stealth model identities by correlating model availability with provider outages and OpenRouter or Vercel AI Gateway incident reports. Use for stealth-model attribution or maintaining an outage evidence ledger.

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。

Annotate speech/TTS datasets with per-clip quality metrics (MOS, SNR, STOI, pitch, bandwidth, speaking rate, WER/CER, gender/emotion), multi-ASR consensus transcription and speaker labeling using the audiogear pipeline; then filter clips by quality and build listenable HTML QA reports. Use when asked to compute audio dataset metrics, build extended_metadata.csv, transcribe unlabeled audio, select clean TTS training data, or QA a speech dataset.

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。

The Rembric TUI installer contract — the single, canonical install/setup/upgrade/uninstall path (`apps/plugin/install.sh`, fronted by the repo-root `install.sh` shim). Apply when touching `install.sh`, the root shim, any per-client `install.sh`/`uninstall.sh`, `marketplace.json`, or distribution docs (README, `docs/agents.md`, plugin/client READMEs). Covers the orchestrator model, what must not break, and where the source of truth lives. For running the validation suite, use `rembric-tui-installer-e2e`.

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。

Runnable end-to-end validation playbook for the Rembric TUI installer — catch breakage before merging/deploying, AND a sandboxed manual walkthrough to evaluate/iterate its UI/UX without polluting your real env. Apply before shipping any change touching `apps/plugin/install.sh`, the repo-root `install.sh` shim, any per-client `install.sh`/`uninstall.sh`, or distribution docs, or when you want to preview/try/demo the installer and polish how it looks and feels. Layered: CI-safe headless (vitest + `sh -n`), local install round-trips, operator interactive pty smoke, an optional full Docker `up` smoke, plus a sandboxed manual UX walkthrough. For the installer contract/what-not-to-break, see `rembric-tui-installer`.

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 and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, and baseline placeholders you can edit before publishing or testing. Use when Codex needs to create a new local plugin, add optional plugin structure, or generate or update repo-root `.agents/plugins/marketplace.json` entries for plugin ordering and availability metadata.

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。

Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.

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。

Maps between gene identifier systems (Ensembl, Entrez, HGNC symbol, UniProt, RefSeq, MANE) using AnnotationDbi, biomaRt, mygene, pyensembl, and Ensembl REST. Encodes Ensembl version stripping with GENCODE _PAR_Y preservation, the Ziemann 2016 Excel autocorrect debacle and Bruford 2020 HGNC renames (SEPT*->SEPTIN*, MARCH*->MARCHF*, MARC*->MTARC*, DEC1->DELEC1), OCT4/POU5F1 alias resolution, biomaRt archive endpoints for release pinning, the `filters` (plural) gotcha, MANE Select for clinical reporting, cross-species orthology via Ensembl Compara / OMA / OrthoDB, and tx2gene construction for tximport. Use when converting gene IDs across systems, handling renamed symbols, building tx2gene, pinning to a specific Ensembl release for reproducibility, or mapping cross-species orthologs.

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。

Work with FASTQ quality scores using Biopython - access Phred scores, filter and trim by quality, compute per-position profiles, and convert between Sanger/Phred+33, Solexa, and Illumina/Phred+64 encodings. Use when analyzing read quality, filtering or trimming low-quality bases, generating quality reports, or deciding which FASTQ quality encoding a file uses before parsing.

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。

Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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。

Build publication-quality figures in R with ggplot2 using the grammar of graphics (data + aesthetics + geometries + scales + facets + themes) with CVD-safe palettes, cairo_pdf TrueType embedding, programmatic aes via tidy evaluation, and the theme_classic publication baseline. Use when producing static figures in R for papers, presentations, or reports.

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。