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Routes memory and retrieval tasks between MemPalace and QMD. Use this skill whenever the user asks WHERE something is in the codebase (route to QMD), WHY something was built a certain way or what was decided (route to MemPalace), or needs BOTH code and historical context (combined). Trigger on phrases like: 'where is X', 'find X in the code', 'search the codebase for X', 'what did we decide about X', 'why does X work this way', 'show me X and why we built it', 'look up X', 'what's in the handoffs about X', 'pull the relevant files for X'. Use proactively whenever a question could benefit from codebase search OR project memory — don't wait for the user to explicitly ask for a search.

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Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.

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Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.

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Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.

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Read this skill before the first step that uses Claude in Chrome, the browser extension whose tools are named mcp__claude-in-chrome__* (also called Chrome, the browser extension, or the external browser) and which acts in the person's real Chrome with their own sign-ins; before those tools are turned on there may be a single enable__mcp__claude-in-chrome tool instead. It covers loading the tools in one ToolSearch call, checking the person's open tabs and working in a new tab, site permissions, GIF recordings, console logs, dialogs to avoid, and when to stop and ask. It is not for the built-in browser (mcp__Claude_Browser__* or mcp__remote-devices__Claude_Browser__* tools), which has its own skill, and it does not decide which browser to use.

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Read this skill before the first step that uses the built-in browser, the browser pane inside the Claude desktop app (also called the in-app browser, the browser pane, Claude's browser, or "your own browser"), whose tools are named mcp__Claude_Browser__* when the session runs in the desktop app and mcp__remote-devices__Claude_Browser__* when a cloud session is linked to the person's computer; before those tools are turned on there may be a single enable__mcp__remote-devices__Claude_Browser tool instead. It covers the pane's persistent sign-ins, tabs and preview_start, reading pages as text, site approvals, what the pane cannot open, and what to do when it cannot be reached. It is not for Claude in Chrome (mcp__claude-in-chrome__* tools), which has its own skill, and it does not decide which browser to use.

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。

One sentence - what this skill does and when to invoke 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。

One sentence - what this skill does and when to invoke 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。

Extract a thesis's load-bearing assumptions and scan for internal contradictions — the falsifiable claims the bull case requires, what would falsify each, and where the thesis's own Risks / Bear Case / research already argue against it. Use when user says "assumptions", "what must be true", "load-bearing assumptions", or "does [TICKER] contradict itself".

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Refresh the Key Metrics table in a thesis with current data from Financial Modeling Prep, with a Quartr standardized-statement tie-break for Revenue Growth / Gross Margin gaps (Step 4b-Q) and a scoped web-search fallback (Step 4b) when FMP has no data or resolved the wrong company. Use when user says "numbers", "refresh metrics", "update key metrics", or "refresh [TICKER]" with no other action implied. Surgical edit only — does NOT create research notes or propagate via /sync. Also flags (never auto-edits) stale price/valuation framing in the thesis Summary — see Step 10b.

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 the Key Metrics table in a thesis with current data from Financial Modeling Prep, with a scoped web-search fallback (Step 4b) when FMP has no data or resolved the wrong company. Use when user says "numbers", "refresh metrics", "update key metrics", or "refresh [TICKER]" with no other action implied. Surgical edit only — does NOT create research notes or propagate via $sync. Also flags (never auto-edits) stale price/valuation framing in the thesis Summary — see Step 10b.

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Map a thesis's position in its value chain — who it buys from, sells to, and competes with; where the bottleneck and pricing power sit; and whether it owns a critical layer. Use when user says "value chain", "supply chain position", "where does [TICKER] sit", or "who are [TICKER]'s suppliers/customers".

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Extract a thesis's dependency fingerprint — the external variables its bull case actually rests on (key customers, suppliers/inputs, technology transitions, macro drivers, single points of failure). Use when user says "dependency map", "what does [TICKER] depend on", "dependency fingerprint", or "map exposures for [TICKER]".

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Investigate implementation failures using root-cause-first debugging. Use when an implementer is blocked, verification fails, or repeated remediation does not converge.

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A clean, product-oriented default. Use when the brief doesn't call for a specific mood — good for B2B tools, dashboards, and utility pages. Content-first, chrome-second.

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Produce a deterministic, read-only evidence map of the repository for SDD Composy (languages, manifests, commands, modules, boundaries, staleness) and publish the context bundle only on request. Use before a PRD or TechSpec, or when the map is stale — 'mapear o repositório', 'atualizar o contexto do código', 'what does this codebase look like for SDD'. Do NOT use for architecture decisions, code review, or repository exploration without an SDD workspace.

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Promote QA → production by opening a PR from peterdrier/Humans:main to nobodies-collective/Humans:main with a properly-qualified commit summary. Use when the user says "PR to production", "promote to prod", "PR from origin to upstream", "prod PR", or any variation of pushing batched fork changes upstream. Always use this skill for the two-remote promotion flow even if the user doesn't say "prod" explicitly.

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Systematically QA test a web application and fix bugs found. Runs QA testing, then iteratively fixes bugs in source code, committing each fix atomically and re-verifying. Use when asked to "qa", "QA", "test this site", "find bugs", "test and fix", or "fix what's broken". Proactively suggest when the user says a feature is ready for testing or asks "does this work?". Three tiers: Quick (critical/high only), Standard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores, fix evidence, and a ship-readiness summary. For report-only mode, use /qa-only. (gstack) Voice triggers (speech-to-text aliases): "quality check", "test the app", "run QA".

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YC Office Hours — two modes. Startup mode: six forcing questions that expose demand reality, status quo, desperate specificity, narrowest wedge, observation, and future-fit. Builder mode: design thinking brainstorming for side projects, hackathons, learning, and open source. Saves a design doc. Use when asked to "brainstorm this", "I have an idea", "help me think through this", "office hours", or "is this worth building". Proactively invoke this skill (do NOT answer directly) when the user describes a new product idea, asks whether something is worth building, wants to think through design decisions for something that doesn't exist yet, or is exploring a concept before any code is written. Use before /plan-ceo-review or /plan-eng-review. (gstack)

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Show ponytail's measured impact as a compact scoreboard: less code, less cost, more speed, from the benchmark medians. One-shot display, not a persistent mode, and not a per-repo number. Trigger: /ponytail-gain, "ponytail gain", "what does ponytail save", "show ponytail impact", "ponytail scoreboard".

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Whole-repo audit for over-engineering. Like ponytail-review, but scans the entire codebase instead of a diff: a ranked list of what to delete, simplify, or replace with stdlib/native equivalents. Use when the user says "audit this codebase", "audit for over-engineering", "what can I delete from this repo", "find bloat", "ponytail-audit", or "/ponytail-audit". One-shot report, does not apply fixes.

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Elite mobile app image-generation skill for creating premium, app-native screen concepts and flows. Designed for iOS, Android, and cross-platform mobile products. Prioritizes clean hierarchy, comfortably readable text, strong multi-screen consistency, controlled color palettes, non-generic creative direction, textured surfaces, image-led composition, tasteful custom iconography, and clean phone mockup framing. By default, screens should be shown inside a subtle premium iPhone or similar phone mockup with a visible frame, while the main focus stays on the app content itself. This skill generates images only. It does not write code.

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Validate process decomposition and architecture design quality before execution begins. Load when the setup-evaluator agent fires (automatic for agent-chain tasks), or when user says "evaluate this setup", "check the decomposition", "validate the architecture", "is this plan sound", "review the agent design". Catches structural errors, missing knowledge, unrealistic step ordering, and topology mismatches. Does NOT modify — only evaluates.

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Pick an icon strategy and produce a coherent icon set that doesn't look mass-produced. Solves the "Lucide everywhere" problem — every AI-generated UI uses the same 24px stroke-1.5 outline icons, which is the second-biggest vibecoded tell after Inter-on-purple-gradient. Generates custom SVG sets, tunes existing icon libraries to match archetype + typography, defines stroke weight, optical sizing, and corner-radius rules. Load when the user asks to pick icons, design an icon set, customize Lucide / Phosphor / Heroicons, generate SVG icons, make icons feel custom, says "the icons look generic", "design a custom icon system", "icons for this product", or when frontend-design routes here during icon craft. Sub-skill of frontend-design.

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。