「stars」の検索結果

Use when the user needs to design, run, or audit product or GTM analytics — metric hierarchies and north stars, funnels, cohort retention, churn, pipeline velocity, forecasting, lead scoring, NRR, A/B tests, or analytical SQL — or uploads a dataset to analyze. Triggers on questions like "why did activation drop", "is this metric meaningful", "review my dashboard", or "what should I measure". For writing the spec that defines a feature's success metrics, use product-spec.

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Menu engineering analysis — item placement, description quality, pricing psychology, upsell opportunities, photo presence, Kasavana matrix (Stars/Plowhorses/Puzzles/Dogs)

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Perform triangulated cognitive research combining SQLite memory, GitHub 10k+ stars patterns, and ToT DAG benchmarking before critical architectural decisions. Automatically triggers subagent research escalation when confidence is low (<0.70). Keywords: research, benchmark, github, memory, triangulation, grounding, best-practices, escalation

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Use this skill when the user wants the full existing-registry Trust Magnitude inspector: understand why a named Gaia skill has a particular star rank, what its Trust Magnitude (TM) score is, which evidence rows are contributing (or dead), how far it is from the next grade, or what evidence to add to move the needle. Also use when asked for a full leaderboard of all named skills ranked by TM. Triggers: "why does X have 3 stars", "what's the TM for X", "inspect TM", "explain the score for X", "what evidence would rank up X", "show leaderboard", "rank all skills by TM", "what's dragging down X's score", "dead evidence rows", "how close is X to grade A/S".

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Dry-run Trust Magnitude for proposed named skills or suites before curation. Use when deciding whether a proposed suite deserves A/S treatment, when checking Fusion Score structural inputs, when comparing repo stars vs suite component counts, or when asked to appraise a candidate before adding it to the registry.

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Phase 2 of the evidence verification pipeline. Validates GitHub star counts in registry skill files against live GitHub API data and flags stale or inflated metrics without repartitioning evidence by rank. Use this after /ev-collection has compiled the unified data lake and before /ev-adversarial-audit begins. Trigger phrases: "verify stars", "check star counts", "refresh stargazer metrics", "run star verification", "ev-star-verification", "Phase 2", "live star check".

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Use this skill when the user wants to understand why a named Gaia skill has a particular star rank, what its Trust Magnitude (TM) score is, which evidence rows are contributing (or dead), how far it is from the next grade, or what evidence to add to move the needle. Also use when asked for a full leaderboard of all named skills ranked by TM. Triggers: "why does X have 3 stars", "what's the TM for X", "inspect TM", "explain the score for X", "what evidence would rank up X", "show leaderboard", "rank all skills by TM", "what's dragging down X's score", "dead evidence rows", "how close is X to grade A/S".

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Focused single-target audit of one Gaia skill, named skill, or real-skill catalog item. Use this skill when: auditing a specific skill by ID or name, a skill looks overpromoted, evidence feels thin or dead, a contributor's skill seems outdated or superseded, the rank doesn't match the source quality, evidence is mapped to the wrong Evidence Type, a skill at 3★+ is missing a working GitHub link, someone asks "does this skill deserve its stars?", or you want the smallest possible correction with validation — not a bulk sweep. This is NOT for bulk curation passes (use /gaia-curate for that) or meta-level registry sweeps (use /gaia-meta-audit). It is specifically for "fix this one skill, get it right."

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Close out a review/meta branch that lands an evidence-ingest / curation batch: create the named nodes, ingest verified evidence, appraise Trust Magnitude, calibrate stars, wire suites, correct upstream naming, stage ONLY the intended artifacts (dropping CRLF churn and blocking leaks), validate, and open the PR. Use when a curation/ingest batch is verified and ready to land, or when the user says "close the review/meta branch", "land the ingest batch", "wrap up the intake PR", "/gaia-review-meta-close". Wraps the deterministic hygiene in scripts/review_meta_close.py and gates the judgment calls (calibration, Origin, suite promotion, naming, merge) behind explicit human checkpoints.

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Set up, format, fix, and clean up LaTeX regression and estimation tables in empirical economics or finance papers. Use when (a) creating a .tex, results.tex, main.tex, or preamble to display regression or estimation output from Python (pyfixest), Stata, or R — including any mention of estout, esttab, estauto, estwide, or table fragments; (b) formatting or repairing estout-style regression tables that overflow the right margin, have cramped or boxed notes, misaligned columns, or poor significance stars, or when asked to match a reference paper, make the note fill the table width, or choose among \estwide, \estauto, and landscape layouts; (c) diagnosing "Misplaced \omit" errors from \input-ed table fragments inside tabular environments. Covers \@@input, \estwide, \estauto, \estautoLT, \specialcell, \sym, minipage notes below tabulars, and the convert-and-verify workflow. Do NOT trigger for general LaTeX documents like beamer presentations, problem sets, letters, or CVs.

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Build interactive "living workflow" flowcharts in the visual style of https://statuesque-starship-49757d.netlify.app/revenue-engine - vanilla HTML/CSS/JS files with a horizontal step bar, a vertical trunk that grows per step, tool-to-tool columns with animated payload pills between brand-marked tool nodes, alternating left/right step descriptions, and per-step outputs. Use whenever the user says "build a flowchart", "interactive flowchart", "living workflow", "revenue engine flowchart", "demand gen flowchart", "GTM workflow diagram", "process diagram with animations", "Nebor-style flowchart", or asks to recreate the workflow visual from the Nebor site. Always asks which flowchart to build (Revenue Engine / Demand Gen / CRM & RevOps / Custom) and waits for explicit approval of the step + tool + edge table before generating any files.

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Discovers custom modules (>100 LOC), analyzes purpose, searches OSS alternatives via MCP Research, evaluates quality (stars, license, CVE), generates migration plan.

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Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al. 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving greater sensitivity to small-effect hits than MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).

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Expert-level astrophysics covering stellar physics, galaxy formation, high-energy astrophysics, observational techniques, and the electromagnetic spectrum across wavelengths.

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Manage agent-skill lifecycles across local, global, project, Skill Hub, skills.sh, GitHub, plugin-cache, and system roots. Use when searching, recommending, installing, updating, upgrading, syncing, merging, auditing, deduplicating, cleaning, packaging, publishing, or validating skills; choosing project versus global scope; verifying upstream source, stars, or installs; or working with skill-upgrade, check_upstreams.ps1, sync-pull/push, junctions, skills-lock.json, or skill-upstreams.json. Do not use for ordinary software-package upgrades or non-skill file cleanup.

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Top 20 GitHub Repositories for AI Agents in 2026 (Ranked by Stars) — 6. Apr. 2026 · Discover the 20 most popular open-source AI agent repositories on

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Terminal-Interface, Desktop-App und IDE-Extension Doku lesen Der Open-Source AI-Coding-Agent [*] Mit über 195,000 GitHub-Stars, 950 Contributors und ü

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Guide for adult content payment processor selection, merchant account application, compliance, webhook integration, and fallback strategies (CCBill, SegPay, AllSecure, Epoch, Telegram Stars, crypto)

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Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).

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。

Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).

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。