Canonical entry point for the full Gaia evidence verification pipeline. Invoke as /evidence-verification-pipeline or /ev-pipeline — both names trigger this skill. Use whenever you need to run the evidence phases end-to-end: collecting raw evidence from the data lake, verifying live GitHub star counts, running Phase 2B benchmark-source verification, running adversarial auditing for noise and URL errors, and checking link health via Firecrawl. Also use when someone says "run the evidence pipeline", "verify the evidence lake", "audit evidence", "run ev-pipeline", "full evidence check", "pipeline run", "prepare evidence for ingestion", or "refresh the data lake". This is the pre-ingestion step — run it before importing any evidence into the registry.
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。
gaia-research/gaia-skill-tree☆ 232026年10月14日 更新
Automates batch reverse engineering by generating Ghidra analyzeHeadless commands and post-analysis script skeletons to import, auto-analyze, and export results for many samples without the GUI. Activates for requests to script Ghidra headless analysis, batch-analyze samples with Ghidra, or generate an analyzeHeadless command and post-script.
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。
meltedinhex/analyst-ai-pack☆ 222026年7月7日 更新
Computes cryptographic and fuzzy fingerprints for malware samples: MD5/SHA-1/SHA-256 for exact identity, plus import hashing (imphash), section hashing, and fuzzy hashes (ssdeep, TLSH) to cluster related variants. Activates for requests to hash a sample, compute imphash, or fingerprint files for similarity clustering.
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。
meltedinhex/analyst-ai-pack☆ 222026年7月7日 更新
Uses Ghidra to disassemble and decompile a binary, navigate to key routines via imports and strings, annotate decompiled code, and run headless scripts to automate extraction of C2, crypto, and config. Activates for requests to reverse engineer with Ghidra, decompile a binary, or script Ghidra headless 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。
meltedinhex/analyst-ai-pack☆ 222026年7月7日 更新
Automates radare2/rizin analysis through r2pipe to script function enumeration, string and import extraction, and cross-reference queries across samples programmatically. Activates for requests to automate radare2 with r2pipe, script rizin analysis, or batch-extract functions and xrefs from binaries.
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。
meltedinhex/analyst-ai-pack☆ 222026年7月7日 更新
Statically analyzes Linux ELF malware: ELF header and sections, dynamic symbols and imports, segment permissions, embedded strings, and packing indicators to infer capability without execution. Activates for requests to analyze an ELF binary, Linux malware, or shared object.
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。
meltedinhex/analyst-ai-pack☆ 222026年7月7日 更新
Compares two related malware samples to surface what changed between variants using section-level hashing, import-set differences, and fuzzy similarity scoring. Activates for requests to diff two samples, compare malware variants, or measure similarity between two binaries for variant tracking.
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。
meltedinhex/analyst-ai-pack☆ 222026年7月7日 更新
Analyzes position-independent code and shellcode by identifying GetPC/PEB-walk techniques, API-hash resolution loops, and relocation-free addressing so the payload can be disassembled at the right base. Activates for requests to analyze position-independent code, identify PIC techniques, or understand how shellcode resolves APIs without imports.
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。
meltedinhex/analyst-ai-pack☆ 222026年7月7日 更新
Detects sandbox and analysis evasion techniques in a sample by scanning static strings, imports, and API traces for VM artifact checks, timing/sleep stalls, anti-debug calls, and environment fingerprinting. Activates for requests to detect sandbox evasion, find anti-VM or anti-debug checks, or explain why a sample did not detonate.
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。
meltedinhex/analyst-ai-pack☆ 222026年7月7日 更新
Baseline, half-leading, and font-face resolution for Label and captured (browser-imported) text — the arithmetic that decides where a glyph lands and how wide the box must be, plus the measure-vs-paint divergences that make text clip or sit low without any test going red.
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。
Generous-Corp/pulp☆ 222026年10月14日 更新
The acceptance bar for anything Pulp can install (a `pulp tool` registry entry, `pulp add` package, or any downloadable). Validate the FULL lifecycle — install AND uninstall, from OUTSIDE a Pulp checkout, with the installed-user's binary — before its README or docs ship. TRIGGER when adding/editing tools/packages/tool-registry.json, a new binary_download/python_pip/npm_package tool, a `pulp add` importer, or writing docs that tell users to install something.
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。
Generous-Corp/pulp☆ 222026年10月14日 更新
Route repository-qualified changes across Pulp and Vellum using Pulp's exact ownership projection. Use when a change touches design import, Chromium authoring, DesignIR, visual harness, screenshot, Skia/Dawn, runtime assets, or another path represented by `.github/vellum-ownership.json`; when deciding whether work originates in Pulp or Vellum; or when validating the cross-repository routing contract.
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。
Generous-Corp/pulp☆ 222026年10月14日 更新
Debug or validate Pulp animations / transitions / scroll behavior using in-process motion fixtures and offline visual analysis. TRIGGER on phrases like "animation is wrong", "transition timing off", "fade too late", "card slides too far", "scroll jumps on restore", "scroll offset wrong on restore", "frame jump in the timeline", "easing looks wonky", "imported Figma motion doesn't match source", "settle time / overshoot / drift / monotonic", "what value does the knob reach at frame N", "record this animation so I can replay it", "this animation is expensive — which one and why", "reduced-motion broken", "scrub a captured fixture". Captures fixtures (.motion.jsonl), and ships assertion helpers (is_monotonic / settling_time_seconds / overshoot / start_delay_seconds / final_value / local_step_outlier_ratio) and the `scroll_geometry` trace for `ScrollView` content-offset / visible-rect / content-size observability. Off by default in prod.
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。
Generous-Corp/pulp☆ 222026年10月14日 更新
Capture faithful PNGs of Pulp view trees / imported UIs headlessly. Covers render_to_png backends (Skia vs CoreGraphics), the image-compositing gotcha, capture_png from a live GPU host, and the --screenshot-backend validate flag. Use whenever you render a UI to a PNG to eyeball or montage 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。
Generous-Corp/pulp☆ 222026年10月14日 更新
Research, author, validate, render, and archive data-only Pulp Sample Heritage profiles. Use when creating or revising a sampler character profile, importing or exporting Heritage JSON, proving profile behavior with analytic and listening fixtures, recording evidence and trademark-safe provenance, or preparing a caller-owned profile artifact bundle.
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。
Generous-Corp/pulp☆ 222026年10月14日 更新
Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.
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。
datit309/supergraph☆ 222026年10月13日 更新
Unity 6 audio system guide. Use when working with sound effects, music, AudioSource, AudioClip, AudioListener, Audio Mixer, spatial audio, or audio optimization. Covers 3D sound, mixer groups, snapshots, and audio import settings. Based on Unity 6.3 LTS documentation.
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。
IdoCohen560/claude-unity-game-studio☆ 222026年7月8日 更新
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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。
shalintripathi/saas-marketing-agents☆ 212026年10月13日 更新
解析用户从 ChatGPT 等 AI 获取的记忆回复(支持多个 AI 依次粘贴),与现有画像对比后,有据可查的条目内部整合直接写入并标注置信度;模糊/冲突/当前需求条目以选择题形式逐条确认;❌记忆不足条目不丢弃,转入步骤五追问队列;写入后执行字段级完整度扫描,列出缺口清单并逐批追问,达到必填门槛后才进入量表阶段。
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。
TashanGKD/tashan-cursor-skills☆ 212026年4月1日 更新
Create, import, analyze, resume, or revise an end-to-end Circuit Weaver electronic design from requirements through validated functional KiCad schematics, placement review, and a truthful manufacturing handoff. Use for new designs and for existing KiCad/PCB/Gerber projects that need durable state. Use kicad for one-off file inspection, bom for standalone sourcing, and design-wizard only when the user explicitly requests the slower questionnaire.
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。
mattpainter701/kicad_automations☆ 202026年10月3日 更新
Create, import, analyze, or resume an end-to-end Circuit Weaver electronic design with durable project state. Load the canonical workflow from skills/circuit-weaver/SKILL.md. Use kicad for one-off file inspection, bom for sourcing, and design-wizard only when explicitly requested.
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。
mattpainter701/kicad_automations☆ 202026年10月3日 更新
Apply the devil's advocate technique: structured critical thinking that stress-tests ideas, plans, and decisions. Use when you need honest critique before committing to something important.
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。
aicodedecode/awesome-muse-skills☆ 202026年10月10日 更新
Plan and protect deep work: scheduling focus blocks, task selection, shutdown rituals, and measuring depth. Use when important work never gets sustained attention or days fill with shallow tasks.
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。
aicodedecode/awesome-muse-skills☆ 202026年10月10日 更新
Fetch skills from projects on the current system, genericize them, and add to the claude-skills repository. Use when user says "sync skills", "fetch skills", "import skills from projects", or wants to pull skills from their local projects into this public repo.
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。
qa-aman/claude-skills☆ 202026年10月13日 更新