58 个跨境电商 · DTC 品牌出海 AI 技能库:Amazon 运营、SEO/GEO、广告归因、财务、联盟、红人、线下零售、VOC、众筹。事实均标来源与日期,内置证据闸——数据不足时说「数据不足」,而不是编一个数字。| 58 skills for cross-border e-commerce & DTC: sourced, dated, with evidence gates. EN/中文. Works with Claude Code & Antigravity.
Cross-Border E-Commerce AI Skills
58 AI-powered skill templates for cross-border e-commerce — from brand strategy to Amazon operations to DTC growth to finance & capital ops to affiliate-program building to EU channel entry to overseas-buyer prospecting + earned-media press discovery + Reddit pre-purchase VOC.
Compatible with Claude Code (~/.claude/commands/), Google Antigravity (SKILL.md), and any AI IDE with skill/prompt support.
English
What is this?
A collection of 58 AI agent skills (structured prompt templates) that automate the entire cross-border e-commerce workflow — brand strategy, market research, product selection, listing optimization, advertising, DTC site operations, finance & capital management, affiliate-program building, EU channel & market entry, social media, influencer marketing, overseas-buyer outbound prospecting, earned-media press discovery, and Reddit pre-purchase VOC.
Two formats:
- Single-file skills (53) — one
.mdfile each, drop into your AI IDE's skill directory. - Multi-file skill packages (5, under
brand-strategy/,outbound-prospecting/, andvoc-tools/) —SKILL.md+references/+templates/(incl. Python scripts and CSV trackers). Point your AI IDE at the package directory.
tools/ (Python utilities used by skills, also runnable independently): backlink-kol-extractor, trustpilot, linktree-expander, contact-extractor, api-pacer, fetchlib, browser-fetch.
Skill Map (58 skills across 13 chains)
┌─────────────────────────────────────┐
│ Brand Strategy Chain (10) │
│ │
Market Scan ──► Track Hypothesis ──► Deep Validation ──► Strategy Plan
│ │
│ Annual Plan ◄── Budget Ops │
│ ▼
│ IMC Framework ──► Knowledge Base
│ │
│ A/B Compare Chart Visualize GTM Launch
│ │
▼ ▼
┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ Amazon Chain (14) │ │ DTC Site (5) │ │ Social & KOL (5) │
│ │ │ │ │ │
│ Selection │ │ SEO Diagnostic │ │ TikTok Growth │
│ Shortlist │ │ SEO Playbook │ │ YouTube Ops │
│ Market Research │ │ SEM Ads │ │ Content Calendar │
│ IP Risk │ │ Conversion UX │ │ Influencer Mktg │
│ Supplier │ └──────────────────┘ │ User Lifecycle │
│ Keywords │ └──────────────────┘
│ Listing Copy │ ┌──────────────────┐ ┌──────────────────┐
│ Main Image │ │ Offline (1) │ │ VOC Tools (3) │
│ A+ Content │ │ │ │ │
│ Compliance │ │ US Retail │ │ Reddit VOC (NEW) │
│ Pre-Launch │ └──────────────────┘ │ Trustpilot Quick │
│ Ad Architecture │ │ Trustpilot Deep │
│ Weekly Ad Review │ └──────────────────┘
│ Ad Diagnosis │
└──────────────────┘
┌──────────────────────────────────────────────────────────────┐ │ Finance & Capital (8) — NEW │ │ Unit-Economics · FX/Payout · Tax-Nexus · Cashflow │ │ Pricing · Reconciliation · Entity-Structure · Capital-Stack │ └──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐ │ Affiliate & Partnership (2) — NEW │ │ Readiness-Audit (affiliate's-POV mirror of CRO) · │ │ Program-Ops (AI: DB → score → outreach → activate → report) │ └──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐ │ Channel & Market Entry (2) — NEW │ │ EU-Channel-Entry (SSPV · RCS · growth flywheel) · │ │ Price-System & Channel-Conflict Guard │ └──────────────────────────────────────────────────────────────┘
Brand Strategy Chain (11 skills)
4-Round Analysis Pipeline + Planning + Execution + Tools
| Skill | What it does | |-------|-------------| | brand-market-scan | Round 1: Market panoramic scan — 4-layer user insight, VOC matrix, competitive landscape, Semrush auto-scan | | brand-track-hypothesis | Round 2: Track hypothesis generation — 3-5 market tracks, DTNICE classification, GTM flywheel | | brand-deep-validation | Round 3: Deep hypothesis validation — 5D framework, SEO traffic model, benchmark cases, Zone 4 | | brand-strategy-plan | Round 4: Brand strategy & execution — 7-element positioning, 4 pillars, pricing, narrative, roadmap | | brand-imc-framework | IMC integrated marketing — Audience/User dual-path, 6-stage funnel, channel mix, execution calendar | | brand-event-marketing | Moment/event marketing — 4 event types, calendar + trend-jacking SOPs, ambush-marketing legal gate | | crowdfunding-launch | Kickstarter/Indiegogo launch — FTC delivery gate, verified fee stack, 4-phase SOP, post-campaign DTC flywheel | | attribution-measurement | Measurement stack — MMM + incrementality + attribution triangulation, geo-test design, Meridian vs Robyn | | brand-annual-plan | Annual planning — BSC scorecard, 52-week calendar, quarterly OKRs, resource allocation | | brand-budget-ops | Budget planning & control — 10-category budget model, monthly tracking, ROI by channel | | brand-knowledge-base | Obsidian knowledge base — batch-creates 30-50 interlinked .md files from all rounds | | brand-ab-compare | 8-dimension A/B quality comparison between two brand strategy report sets | | brand-chart-visualize | Auto-generate charts (radar, bar, waterfall, scatter, etc.) via AntV API for all reports | | report-pdf-export | Convert any finished report Markdown into a house-styled PDF (A4 landscape, dark-blue headers, zebra rows, page numbers). The most-reused skill in the repo — 37 other skills chain into it for their final deliverable |
Amazon Operations Chain (14 skills) — UPGRADED v3.14 (2026 algorithm & policy refresh)
| Phase | Skill | What it does | |-------|-------|-------------| | Selection | amazon-product-selection | Score and rank Top 30 potential products | | Selection | amazon-product-shortlist | Feasibility screening, GTM flywheel check, Go-List | | Research | amazon-market-research | Full market research: VOC matrix, competitor teardown, SWOT | | Research | amazon-ip-risk-assessment | Patent + trademark risk, design comparison, risk rating | | Research | amazon-supplier-decision | Supplier evaluation, cost breakdown, red flag detection | | Listing | amazon-keyword-research | Keyword library: 3-tier CPC, COSMO + Alexa-for-Shopping AI-search SEO | | Listing | amazon-listing-copywriter | Title + Item Highlights (3 candidate versions each), bullet points, A+ description, Search Terms | | Listing | amazon-main-image-prompt | Main + secondary image design briefs and AI prompts | | Listing | amazon-aplus-image-prompt | A+ Content module layout, Brand Story, image prompts | | Launch | amazon-compliance-review | 3-dimension audit: platform rules, legal/IP, AI-agent readiness | | Launch | amazon-pre-launch-review | Final pre-launch checklist across all SKILL outputs | | Ads | amazon-ad-architecture | PPC structure: SP/SB/SD + Prompts ads, AMC, placement/bid strategy | | Ads | amazon-weekly-ad-review | Weekly ad review: ACoS/TACoS, Search Terms, action list | | Ads | amazon-ad-diagnosis | Existing product diagnosis: 4-stage optimization pipeline |
DTC Site & Traffic (5 skills)
| Skill | What it does | |-------|-------------| | dsite-seo-diagnostic | NEW — Entry-orchestrator skill for live-site SEO traffic-drop diagnostics. 7-dimension diagnosis (traffic curve / keyword loss / single-point risk / i18n pollution / backlink quality / content ROI / KPI audit) → algorithm-event alignment → restart roadmap. Chains xlsx / dsite-seo-playbook / trustpilot-voc-deep / competitors-analysis / backlink-kol-extractor / report-pdf-export. Output: deliverable PDF (A4 landscape, hides internal SKILL refs). | | dsite-seo-playbook | Full SEO playbook: technical audit, keyword strategy, content plan, Core Web Vitals | | dsite-sem-ads | SEM & paid ads: 10-platform comparison, AIPL funnel, budget allocation | | dsite-conversion-ux — UPGRADED v3.7 | Live-site CRO audit / 转化率检测, rebuilt for multi-agent concurrency on Claude Code. Step 0 fans out 5 parallel recon subagents (PDP / discovery / trust / checkout / competitor) via the Workflow tool with StructuredOutput schemas → reconPool → parallel 6-module framework (trust / discovery / product-info / checkout / AOV-LTV / urgency) — retail-analogy CRO × Shopify benchmarks × ICE A/B × AICPL LPO × 35-day welcome flow. v3.7 adds a technical-health & tracking-integrity check (console errors / pixel firing / CWV / 404 / mobile-sticky — broken tracking silently invalidates all measurement → highest-priority finding), a mandatory copy-rewrite table (current → A/B) + objection-handling table, a quick-win vs high-impact dual-bucket with wall-clock effort, and A/B stop-rule discipline. Honesty preserved — data-credibility statement kept, lift figures stay ⚠️ hypotheses (no fabricated funnel numbers). Uses browser-class MCP (Claude in Chrome / Preview) for what static fetch can't see; chains /dsite-seo-playbook / /dsite-sem-ads / /report-pdf-export. | | serp-content-teardown — NEW v3.6 | Multi-file skill: deterministic (no-LLM) SERP/content reverse-engineering from local Semrush xlsx + competitor HTML. Parses serpurls + broad-match, curl-fetches top competitor articles (html5lib void-tag-safe), computes per-article structure → 8 article archetypes + opening/closing patterns, then keyword distribution + core keywords, backlink/authority thresholds (Page AS / Ref.Domains / Backlinks) + weak-link winners, AI-Overview (GEO) saturation + schema readiness, on-page SEO. Output: per-topic content blueprint (which archetype / word / H2 / schema / FAQ / keyword / authority / GEO posture). Pairs with backlink-kol-extractor (links) + structured-data-buildout (schema). |
Finance & Capital (8 skills) — NEW v3.8
Operator-facing finance/CFO chain for China→US/EU DTC + Amazon sellers — the actuals / profitability / tax / cash / financing side that the planning-oriented brand-budget-ops doesn't cover. Every skill is grounded in real tools + 2026 regulations, carries an explicit YMYL disclaimer (planning aid, NOT professional tax/legal/accounting advice — verify with a licensed CPA), and tags every rate/threshold/date as point-in-time. Built and cross-validated from a dual-source research pass (multi-agent web research + an independent deep-research report).
| Skill | What it does | |-------|-------------| | finance-landed-cost-unit-economics | Anchor — fully-loaded landed cost → CM1/CM2/CM3 waterfall → break-even ROAS/ACoS → per-SKU keep/kill/reprice + tariff/return sensitivity. Folds in Amazon 2026 fee changes (inbound placement ↑, Low-Inventory-Level Fee, fuel/inflation surcharge). | | finance-fx-payout-optimizer | Collection · 结汇 · FX hedging. The "two numbers" (gross revenue vs RMB cash-landed) + FX-drag %, provider benchmark (PingPong/WorldFirst/Airwallex/Payoneer/Wise vs ACCS), natural-hedge plan, and a SAFE / 单证一致 compliance checklist (incl. the 2026-01-01 ≥¥5,000 AML monitoring). | | finance-tax-nexus-vat-diagnostic | Per-jurisdiction registration-obligation map + filing calendar + EPR streams. Covers EU OSS/IOSS, the €3 fixed customs duty (Reg (EU) 2026/382 — kept distinct from the not-yet-law ~€2 handling fee), UK £135, US economic nexus, and China 9610/9710/9810 + 出口退税. | | finance-cashflow-runway-forecaster | CCC (DIO/DSO/DPO) + a 13-week rolling direct-method forecast that models the Amazon DD+7/DDBR reserve correctly + a reorder-point capital trade-off → flags the week cash goes negative. | | finance-pricing-margin-guard | Dynamic price floor/ceiling, break-even ROAS = 1/CM%, FX-sensitivity-per-1%, marketplace price-parity + anti-gouging guardrails — fixes the static-floor trap when 2026 fees rise. | | finance-reconciliation-bookkeeping | Multi-channel gross-to-net reconciliation (Amazon settlement / Shopify-Stripe-PayPal payouts → bank) via the clearing-account-to-zero method, an ecommerce chart of accounts, COGS/inventory valuation. Tools: A2X / Link My Books / Synder / Finlens → QuickBooks / Xero. | | finance-entity-structure-advisor | Entity tier (single HK Ltd → ODI → HK/SG holding → US LLC) with a "you don't need this yet" guardrail, the LRD transfer-pricing model, HK TP-doc exemption, and the 4-gate China profit repatriation. YMYL-heavy. | | finance-capital-stack-advisor | Normalizes any RBF/MCA flat-fee quote to a true effective APR at real repayment speed (Monte-Carlo), a provider-fit matrix (Wayflyer/Clearco/8fig/Amazon Lending), UCC-1-lien / exclusivity contract traps, and chargeback/VAMP defense. |
Social Media & Content (3 skills)
| Skill | What it does | |-------|-------------| | tiktok-growth | TikTok full-funnel growth: content strategy, TikTok Shop, livestream, paid ads | | youtube-channel-ops | YouTube channel operations: content strategy, SEO, monetization | | social-content-calendar | Social media content calendar: multi-platform scheduling, content pillars |
VOC & Review Analysis (3 skills) — NEW v3.5 adds pre-purchase VOC
VOC tools split by decision stage. Reddit / Quora capture pre-purchase intent (still-deciding users), while Trustpilot / Amazon Review capture post-purchase experience (already-bought users). Use them together for full decision-funnel coverage.
| Skill | What it does | |-------|-------------| | reddit-voc — NEW v3.5 | Multi-file package for pre-purchase Reddit VOC mining. 5-step playbook (find subs across 4 dimensions → filter Top + 6 post-flair → 6-axis post teardown → insight-to-action mapping → optional 2D positioning matrix). References: 4-dimension community framework / Reddit slang dictionary (BIFL / YMMV / AITA / DAE / etc.) / 6-class post taxonomy with business-action mapping / 3 real listing+ad rewrite cases / functional-importance × satisfaction matrix. CSV templates for community map, post analysis, insight-action map. Pairs with /trustpilot-voc-deep for post-purchase view. | | trustpilot-voc-quick | 5-min WebFetch scan: overall rating, star distribution, recent review summaries. Ideal for brand scanning Step 0 or competitor comparison | | trustpilot-voc-deep | Full pipeline (15-40 min): Selenium scraper with proxy rotation + sentiment analysis + LDA topic modeling + AI-powered deep insights. Uses tools/trustpilot/ Python toolkit with AntV visualization for report-style consistency |
KOL & User Operations (2 skills)
| Skill | What it does | |-------|-------------| | influencer-marketing | KOL/influencer marketing: 5-tier pyramid, ROI tracking, contract templates | | user-lifecycle-ops | User lifecycle management: 5-stage funnel, retention curves, churn analysis |
GTM & Offline (2 skills)
| Skill | What it does | |-------|-------------| | brand-gtm-launch | New product GTM launch: 7-step framework, timeline, channel coordination | | offline-retail-us | US offline retail: 8-tier channel analysis, readiness assessment, cost model |
Affiliate & Partnership (2 skills) — NEW v3.13
Building an affiliate/partnership program from the affiliate's economics — audit first (is your product even worth promoting?), then run the AI-automated recruit → score → outreach → activate loop. Complements influencer-marketing (content/relationship lens) without overlap: this is the performance/ROI lens.
| Skill | What it does | |-------|-------------| | affiliate-readiness-audit — NEW v3.13 | Audits a product/Listing from the affiliate's wallet POV — the mirror of dsite-conversion-ux (CRO): CRO audits "why users won't buy," this audits "why affiliates won't promote you." Affiliate ROI ledger + EPC + 3-layer arbitrage, 6-dim recruitability scorecard with score bands, admission buckets, 6 silent-rejection red flags, traffic-catch self-check + 30-day fix roadmap. Multi-agent fan-out over the 6 scoring dimensions + competitor-offer benchmark | | affiliate-program-ops — NEW v3.13 | AI-automated affiliate operations pipeline: database → find candidates across the 6 affiliate ecosystems → 100-pt AI scoring with priority bands → standardized contact collection (10 sources) → AI personalized outreach (5-element email, not mass blast) → Partner Resource Hub activation → 4-layer KPI + AI weekly report → 30-day minimum loop. Fan-out per ecosystem type via Workflow; reuses outbound-prospecting infra + backlink-kol-extractor |
Channel & Market Entry (2 skills) — NEW v3.13
| Skill | What it does | |-------|-------------| | eu-channel-market-entry — NEW v3.13 | Channel/distributor-driven Europe market entry for US-entity brands: US-vs-EU market logic (growth vs mature-replacement), competitive-positioning quadrant, SSPV user-value model (JTBD-based), RCS regional-combat model (Region × Channel × Service) across the 5 EU regions, channel development + enablement, price protection, overseas-org 3 stages, the 8-step Europe growth flywheel. Org/tax decisions hand off to finance-*. 5-region parallel analysis via Workflow | | price-system-conflict-guard — NEW v3.13 | Multi-channel price-system & channel-conflict guard for brands running DTC + Amazon + distribution simultaneously: cross-channel real-price diagnosis (auto-flags conflicts), the 3-layer mechanism (unified price × promo-sync × regional protection), DTC-vs-channel boundary, T-60 promo SOP. Carries a US antitrust (MAP vs RPM / Sherman Act) compliance boundary. Pluggable impl of the flywheel's price-protection step |
Outbound Prospecting (3 skills) — v3.4 adds press discovery
End-to-end pipelines for finding overseas B2B decision-makers, KOLs, and journalists, then converting them into ready-to-message lead sheets. Each skill is a multi-file package with SKILL.md + scripts + references + templates.
| Skill | What it does | |-------|-------------| | google-whatsapp-prospecting | Google-dork → WhatsApp lead pipeline. 15+ search formulas (mobile-prefix narrowed), 30+ countries with B2B-platform / time-zone / compliance flags, full GDPR-CASL-CCPA-UWG compliance reference, multi-language outreach playbook (EN/ES/PT/FR/AR), SerpAPI batch script + wa.me validator. | | linkedin-prospecting | Google/Bing/Yandex/Wayback reverse-search of LinkedIn → enrichment via Apollo/Snov/Hunter/Lusha/Wiza → 4-touch outreach (CR → DM → follow-up → channel-switch). 50+ localized role keywords across 8 languages, LinkedIn ToS + quota reference, 12 DM templates across 5 archetypes (incl. voice-note opener), reply-handling matrix. | | media-press-discovery — NEW v3.4 | Muckrack-anchored journalist DB pipeline. 5 scripts (discoverjournalists / findarticles / guessemails / scoreandexport / mergepartitions) + shared fetcher.py with 4 backends (requests / remote-chrome / apify / html-dir for Cloudflare-protected pages). Multi-machine partition-merge workflow. Outputs ranked pitch_db.csv with journalist contacts + last topical coverage. |
Sister skills — same 4-stage shape (Search → Enrich → Outreach → Compliance), different channels. Designed to run in parallel for the same lead set.
Tools (standalone utilities)
Standalone Python utilities under tools/. Each is a multi-file package with own SKILL.md + scripts/ + references/ + templates/. Used by skills above conditionally; also runnable independently. The three fetch tools — api-pacer, fetchlib, browser-fetch — compose into one compliant scraping pipeline (pace → waterfall → browser L3); the rest are data/enrichment utilities.
| Tool | What it does | Used by | |------|-------------|---------| | backlink-kol-extractor | Extract KOL / media / affiliate prospects from Semrush competitor backlink xlsx data — 3-step methodology (domain pattern → cross-competitor validation → social handle extraction) | influencer-marketing (Step 2.5), dsite-seo-playbook (Step 4.6) — both conditionally activated when Semrush data is provided | | trustpilot — rebuilt v3.4 | Selenium-based Trustpilot review scraper with chained-proxy rotation, AI sentiment + topic analysis, multi-language. v3.4 rebuild: modern data-* attribute selectors (replaces 110-line sibling-XPath fallback chain), desktop-UA pin (Trustpilot serves snippet-only DOM to mobile UA), --cutoffdate arg, --skip_ai mode, redacted hardcoded proxy creds (env-var loading) | trustpilot-voc-quick, trustpilot-voc-deep | | linktree-expander — NEW v3.4 | Batch-enrich Linktree handles into per-creator profiles via NEXTDATA JSON parsing. Extracts IG / TikTok / YouTube / Substack / Twitter / podcast handles + bio + outbound link categorization + handle-match-scored personalsite (with NONPERSONAL_HOSTS blocklist for shorteners / aggregators / docs / scheduling) | KOL discovery pipelines downstream of backlink-kol-extractor | | contact-extractor — NEW v3.4 | Multi-source contact email extraction with confidence tiering. Sources: personalsite /about /contact /press paths (mailto/text) + YouTube Data API v3 description + Apple Podcasts RSS owner + email pattern guess (with --verify SMTP MX probe / Hunter.io). Outputs ranked contactemail1..3 + confidence (high / medium / low / none) | KOL outreach prep, post linktree-expander or media-press-discovery | | api-pacer — NEW v3.9 | Polite, adaptive request pacer + AWS-style full-jitter backoff. Paces to the server's own x-ratelimit-* headers when present, else a configured RPS budget. Stdlib-only; rate-limit-respecting research use only. Rationale + usage in SKILL.md. | reddit-voc (wired) + serp-content-teardown / media-press-discovery / trustpilot / outbound-prospecting (opt-in) | | fetchlib — NEW v3.10 | Compliant fetch waterfall — escalates one tier only on a real block: L1 curlcffi → L2 Jina Reader → L3 browser → L4 managed (paid, opt-in). Control = api-pacer + AIMD + circuit breaker + Thompson-sampling backend selector. Honors robots; no barrier-defeat / IP-rotation / PII. Tiers, benchmarks + compliance red line in SKILL.md. Depends on api-pacer. | serp-content-teardown / media-press-discovery / trustpilot / outbound-prospecting / reddit-voc (opt-in) | | browser-fetch — NEW v3.12 | Optional shared browser-render backend for fetchlib's L3 (pluggable engine, default Selenium). Clears Trustpilot-class JS sites but not Cloudflare / DataDome fortresses (those need a residential IP or paid unblocker — full honest-scope benchmark in SKILL.md). No CAPTCHA-solving / barrier-defeat / PII; tools/trustpilot left untouched. | any JS-render need via fetchlib browser tier (opt-in) |
See tools/README.md for standalone usage.
Key Features
- 58 Skills, 13 Chains — Complete coverage from brand strategy to daily operations to finance & capital to affiliate-program building to EU channel entry to overseas-buyer outbound to earned-media press discovery to pre-purchase Reddit VOC
- Affiliate & Channel Systems (2026 decks) — a 2-skill affiliate chain built from the affiliate's own economics (
affiliate-readiness-auditmirrors CRO — "why affiliates won't promote you";affiliate-program-opsruns the AI recruit→score→outreach→activate loop), plus a US-entity Europe channel-entry strategy (SSPV / RCS / growth flywheel) and a multi-channel price-conflict guard. Framework skeletons only, each crediting its source deck in a methodology index - Finance & YMYL Discipline — the 8-skill finance chain carries explicit "planning aid, not professional tax/legal/accounting advice — verify with a CPA" disclaimers, point-in-time-stamped 2026 regulations, and ⚠️-flagged estimates (no fabricated numbers)
- Data Verification Layer — Every skill includes mandatory verification; estimates are explicitly flagged with ⚠️
- Chart Visualization — 21 skills auto-generate charts (radar, bar, waterfall, scatter, funnel, etc.) via AntV API
- Semrush Integration — Brand strategy skills auto-scan local Semrush xlsx/PDF data as high-confidence source
- VOC Matrix — Mention frequency × satisfaction matrix to identify unmet needs
- GTM Flywheel — Market → Product → Marketing → Operations four-wheel evaluation
- AI Search Ready — Optimized for Amazon Rufus, COSMO knowledge graph, and GEO
- Multi-Agent Concurrency (Claude Code-native) — skills like
dsite-conversion-uxorchestrate parallel recon + analysis subagents via theWorkflowtool withStructuredOutputschemas, run as background tasks, and use browser-class MCP (Claude in Chrome / Preview) for live-site inspection — fan out the work, keep the conclusions - Compliant Scraping Stack (2026-benchmarked) — a free-first, US-compliant fetch pipeline the skills share:
api-pacer(pacing + backoff) →fetchlib(waterfall:curl_cffi→ Jina Reader → browser → paid) →browser-fetch(browser-render L3). Honors robots + rate limits; no barrier-defeat / IP-rotation / CAPTCHA-solving / PII. Benchmarks, tier mechanics, and the full compliance red line live in each tool'sSKILL.md - Linted, not just written — every skill carries
name+descriptionfrontmatter (the triggering surface), and aSkill lintCI gate blocks four regressions this repo actually hit: missing/mismatched frontmatter, the same skill living at two paths (copies drift apart silently), dead relative links, and any advice that would break the scraping red line. Run it locally withpython3 scripts/lint_skills.py - Multi-Platform — Works on Claude Code, Google Antigravity, OpenClaw, and any AI IDE
Installation
Claude Code (recommended):
git clone https://github.com/noique/cross-border-ecommerce-skills.git
Single-file skills → ~/.claude/commands/
cp cross-border-ecommerce-skills/brand-strategy/*.md ~/.claude/commands/
cp cross-border-ecommerce-skills/amazon/*.md ~/.claude/commands/
cp cross-border-ecommerce-skills/finance/*.md ~/.claude/commands/
Multi-file skill packages → ~/.claude/skills/ (one directory per skill)
cp -r cross-border-ecommerce-skills/outbound-prospecting/google-whatsapp-prospecting ~/.claude/skills/
cp -r cross-border-ecommerce-skills/outbound-prospecting/linkedin-prospecting ~/.claude/skills/
cp -r cross-border-ecommerce-skills/outbound-prospecting/media-press-discovery ~/.claude/skills/
cp -r cross-border-ecommerce-skills/voc-tools/reddit-voc ~/.claude/skills/
cp -r cross-border-ecommerce-skills/brand-strategy/serp-content-teardown ~/.claude/skills/
cp -r cross-border-ecommerce-skills/tools/backlink-kol-extractor ~/.claude/skills/Google Antigravity / OpenClaw / Any AI IDE: Copy .md files (single-file skills) or whole directories (multi-file packages) into your skill directory.
Model Requirements
Judge by capability, not by model name. What these skills actually need is three things — the model list below expires every few months, this bar doesn't:
| Capability | Why these skills need it | |---|---| | Instruction-following across long context | A single skill runs 10-50KB; the model has to still follow the structure after reading the whole spec | | Actually executing multi-step verification | The library leans hard on self-checks — count the bytes, cite the source, mark ❌未获取 when it's missing. A model that skips verification and jumps to the conclusion turns every honesty guardrail into decoration | | Short-delivering honestly instead of filling the gap | When evidence is thin it has to write "insufficient data, no conclusion" rather than inventing a plausible number to complete the table |
🔴 The second and third are the dividing line. Following a structure is easy — plenty of mid-size models emit a report that looks complete. The hard part is leaving blank what should be blank. Test a candidate model by feeding it an input with a key figure missing and seeing whether it flags the gap or invents a number.
🔴 "The skill doesn't run" — check the runtime before the skill
Maintainer's field observation (not a controlled benchmark): these skills behave best on stock Claude Code running against Anthropic's own API/subscription. Reports of a skill "not working" have mostly traced back to the runtime, not the skill file — Claude Code pointed at a third-party relay or reseller subscription, or a different harness (Codex-style and other agent CLIs) loading the file.
Why the runtime matters more here than for a plain prompt:
| What the skills rely on | What a substituted runtime often breaks | |---|---| | The harness loading SKILL.md and honoring its frontmatter | SKILL.md + frontmatter is a Claude Code convention. Another harness may treat the file as plain context — or not load it at all — so the skill silently never activates | | Full-fidelity long context (10-50KB per skill, plus your data) | Relays commonly truncate, compress, or re-chunk context. The verification rules live throughout the file — drop the tail and the honesty gates go with it | | A specific model actually serving the request | Some resellers route "Claude" traffic to a different or quantized model without saying so. You get output shaped like the skill with none of the self-checking behavior | | Multi-step self-verification surviving the system prompt | Harnesses that rewrite or prepend their own system prompt can override the skill's verification instructions |
Self-diagnose in this order before filing an issue:
- What's actually serving you? Confirm the runtime and that requests hit the model you think they do — not a relay in between.
- Did the skill load at all? Ask the agent to restate the skill's required output sections. If it can't, the file never activated — that's a loading problem, not a skill problem.
- Run the blank test. Feed an input with a key figure missing. Correct behavior is flagging the gap (
❌未获取/ "insufficient data"). If it invents a number, the honesty scaffolding isn't running, whatever the model claims to be.
This is one maintainer's testing across the setups they had access to, not a benchmark of every provider. Third-party relays vary widely; some may be fine. The point is that the runtime is the first thing to rule out, because it accounts for most of the "doesn't work" reports.
Reference models (verified 2026-08 — will go stale)
| Tier | Quality | Models at the time | |------|---------|--------------------| | Recommended | Full execution, verification works | Claude Opus 5 / Sonnet 5 (and Fable 5), GPT-5.6 Sol / GPT-5.5 family, Gemini 3.1 Pro | | Usable | Structure OK, may skip verification | DeepSeek V4, Qwen 3.5, GLM-5.2, Llama 4, Claude Haiku 4.5 | | Not recommended | Sections missing, checks fail | Small models (empirically, under ~30B) |
⚠️ This table goes stale by design — the previous version sat at April 2026 and was already two Claude generations behind. Don't copy the names; use the capability bar above to test whatever you have. Current lineups: Anthropic · OpenAI · Google.
The Claude row comes from Anthropic's own docs; the rest is public reporting collated 2026-08 — vendors ship on different cadences, so treat each vendor's docs as authoritative.
Key requirements: long context (8K+ input), strong instruction following, Chinese-English bilingual, tool use / web browsing.
中文说明
这是什么？
一套 58 个跨境电商 AI 技能模板，覆盖品牌战略→选品→调研→文案→广告→独立站→财务资金→联盟营销→欧洲渠道进入→社媒→红人→线下渠道→海外开发→媒体公关→购买前 Reddit VOC 全流程自动化。
两种格式：
- 单文件技能（53 个） — 一个
.md文件，放入 AI IDE 技能目录即可使用 - 多文件技能包（5 个，分布在
brand-strategy/、outbound-prospecting/和voc-tools/） —SKILL.md+references/+templates/（含 Python 脚本和 CSV 跟踪表），将整个目录指向 AI IDE
tools/（Python 工具，被 skill 调用也可独立使用）：backlink-kol-extractor / trustpilot / linktree-expander / contact-extractor / api-pacer / fetchlib / browser-fetch。
技能矩阵（58 个技能，13 条链路）
| 链路 | 数量 | 技能 | |------|------|------| | 品牌战略链 | 11 | 市场扫描 → 赛道假设 → 深度验证 → 品牌战略 → IMC框架 → 年度规划 → 预算管控 → 知识库 → A/B对比 → 图表可视化 → 报告 PDF 导出（全库被复用最多，37 个技能链它出终稿） | | Amazon 运营链 | 14 | 选品 → 筛选 → 调研 → IP排查 → 供应商 → 关键词 → 文案 → 主图 → A+ → 合规 → 复查 → 广告架构 → 周报 → 诊断 | | 独立站流量 | 5 | SEO 全链路诊断（NEW v3.3）→ SEO全链路规划 → SEM广告 → 转化率优化 CRO（UPGRADED v3.7，多 Agent 并发实站检测：第零步 5 子代理并发侦察 + 6 模块 + 技术追踪健康层 + 文案改写/异议表 + 速赢双桶，Claude Code Workflow 编排） → SERP 内容拆解（NEW v3.6，竞品文章结构 + 关键词 + 反链 + GEO 一起拆） | | 财务与资金（NEW v3.8） | 8 | 落地成本与单位经济(CM1/CM2/CM3) → 收款·结汇·FX 对冲 → 税务合规(Nexus/VAT/IOSS/EPR，含 EU €3 关税与 ~€2 处理费之分) → 13 周现金流预测 → 定价·毛利护栏 → 多渠道对账记账 → 跨境架构与利润回流 → 融资真实成本与风控 | | 社媒与内容 | 3 | TikTok增长 → YouTube运营 → 内容日历 | | VOC 评论分析 | 3 | Reddit VOC（NEW v3.5，购买前用户洞察 / 4 维度找社区 / 6 类帖子分类 / 黑话词典 / 矩阵定位） → Trustpilot 快速扫描 → Trustpilot 深度分析（爬虫+情感+LDA+AI 归纳） | | 红人与用户 | 2 | 红人营销 → 用户生命周期 | | GTM 执行 | 1 | 新品上市规划 | | 线下渠道 | 1 | 美国线下零售 | | 联盟与合作伙伴（NEW v3.13） | 2 | 联盟可推性审计（联盟客视角，dsite-conversion-ux CRO 的镜像——审"联盟客为什么不推你"：ROI 账本 + EPC + 三层套利 + 六维评分 + 沉默拒绝红灯）→ 联盟运营流水线（AI）（数据库→6 类生态找候选→100 分评分→10 来源采集→AI 个性化建联→素材库激活→4 层 KPI→30 天闭环，多 Agent 并发） | | 渠道与市场进入（NEW v3.13） | 2 | 欧洲渠道进入战略（美国主体进欧盟：美/欧市场逻辑 · 定位象限 · SSPV 用户价值 · RCS 区域作战 · 渠道赋能 · 组织三阶段 · 增长飞轮，5 区并发）→ 价格体系与渠道冲突护栏（DTC+Amazon+分销：跨渠道真实价诊断 · 统一价×促销同步×区域保护三层 · T-60 促销 SOP · 含 MAP/反垄断边界） | | 海外开发与媒体公关（NEW v3.2 + v3.4） | 3 | Google→WhatsApp 反查开发 → Google→LinkedIn 反查开发 → 媒体公关发现（NEW v3.4，Muckrack-anchored journalist DB pipeline，5 脚本 + Cloudflare-aware 4 后端 fetcher + 多机分片） |
核心特色
- 58 技能 × 13 链路 + 7 独立工具 — 从战略到执行到财务资金到联盟营销到欧洲渠道进入到海外开发到媒体公关到购买前 Reddit VOC 全覆盖
- 联盟与渠道体系（源自 2026 行业分享） — 从联盟客的经济账反推的 2 技能联盟链（
affiliate-readiness-audit是 CRO 的镜像——审"联盟客为什么不推你"；affiliate-program-ops跑 AI 招募→评分→建联→激活闭环），外加美国主体的欧洲渠道进入战略（SSPV / RCS / 增长飞轮）与多渠道价格冲突护栏。只抽方法论骨架，每个技能在"参考方法论索引"注明来源分享 - 财务链 YMYL 纪律 — 8 个财务技能均带"规划辅助、非专业税务/法律/会计意见、需找 CPA 核实"免责，2026 法规打时间戳，估算标 ⚠️（不编造数字）
- 数据验证层 — 每个技能内置强制验证，推测数据标 ⚠️
- 图表可视化 — 21 个技能自动生成图表（雷达/柱状/瀑布/散点/漏斗等），调用 AntV API
- Semrush 集成 — 品牌战略技能自动扫描本地 Semrush 数据
- VOC 用户洞察 — 提及量×满意度二维分析
- GTM 飞轮 — 市场→产品→营销→运营四维评估
- AI 搜索适配 — Amazon Rufus / COSMO / GEO 优化
- 多 Agent 并发（Claude Code 原生） —
dsite-conversion-ux等技能用Workflow工具编排并发侦察+分析子代理（StructuredOutputschema），后台任务运行 + 浏览器类 MCP（Claude in Chrome / Preview）做实站检测——把活儿 fan out，只留结论 - 合规抓取栈（2026 实测） — 各 skill 共享的免费优先、美国合规取页流水线：
api-pacer（限速 + 退避）→fetchlib（waterfall：curl_cffi→ Jina Reader → 浏览器 → 付费）→browser-fetch（浏览器渲染 L3）。默认守 robots + 限速，不破壁 / 不换 IP / 不解 CAPTCHA / 不碰 PII。基准数据、分层机制与完整合规红线见各工具SKILL.md - 有 lint 兜底,不只是写完 — 每个技能都带
name+descriptionfrontmatter(技能触发面),并有Skill lintCI 门禁拦住本仓踩过的四类回归:frontmatter 缺失/名不符实、同一技能存在于两个路径(副本会静默分叉)、相对链接失效、以及任何违反抓取红线的写法。本地跑:python3 scripts/lint_skills.py - 多平台兼容 — Claude Code / Antigravity / OpenClaw / 任何 AI IDE
安装方式
# Claude Code 一键安装
git clone https://github.com/noique/cross-border-ecommerce-skills.git
单文件技能 → ~/.claude/commands/
cp cross-border-ecommerce-skills/brand-strategy/*.md ~/.claude/commands/
cp cross-border-ecommerce-skills/amazon/*.md ~/.claude/commands/
cp cross-border-ecommerce-skills/finance/*.md ~/.claude/commands/
多文件技能包 → ~/.claude/skills/（每个技能一个目录）
cp -r cross-border-ecommerce-skills/outbound-prospecting/google-whatsapp-prospecting ~/.claude/skills/
cp -r cross-border-ecommerce-skills/outbound-prospecting/linkedin-prospecting ~/.claude/skills/
cp -r cross-border-ecommerce-skills/outbound-prospecting/media-press-discovery ~/.claude/skills/
cp -r cross-border-ecommerce-skills/voc-tools/reddit-voc ~/.claude/skills/
cp -r cross-border-ecommerce-skills/brand-strategy/serp-content-teardown ~/.claude/skills/
cp -r cross-border-ecommerce-skills/tools/backlink-kol-extractor ~/.claude/skills/模型要求
先看能力门槛，再看型号。 这些 skill 对模型的真实要求是三条——型号表每几个月就会过期，能力门槛不会：
| 能力 | 为什么这些 skill 需要它 | |------|----------------------| | 长上下文里的指令遵循 | 单个 skill 有 10-50KB，模型要在读完整份规格后仍然照着结构走 | | 多步验证的执行力 | 本仓大量依赖「先算字节数 / 先查来源 / 缺了就标 ❌未获取」这类自检步骤。模型如果倾向于跳过验证直接给结论，诚信护栏就是摆设 | | 如实短交而非补齐 | 证据不足时要能写「数据不足，不下结论」，而不是编一个像样的数字把表格填满 |
🔴 第二条和第三条是本仓的分水岭。 结构照着走很容易，很多中小模型都能生成看起来完整的报告——难的是该空着的时候空着。选型时请用这个测：给它一份缺关键数据的输入，看它是标注缺口，还是编一个数字。
🔴 「skill 跑不通」——先排查运行环境，再怀疑 skill
维护者实测口径（非对照评测）： 这些 skill 在原版 Claude Code + Anthropic 官方 API/订阅下表现最好。收到的「跑不通」反馈，绝大多数最后定位到的是运行环境而不是 skill 文件本身——要么 Claude Code 接的是第三方中转/转售订阅，要么换了别的 harness（Codex 一类的 agent CLI）在读这个文件。
为什么这里比普通 prompt 更吃运行环境：
| skill 依赖什么 | 换掉运行环境后常见的断点 | |---|---| | harness 真的加载 SKILL.md 并认 frontmatter | SKILL.md + frontmatter 是 Claude Code 的约定。别的 harness 可能只当普通上下文、甚至根本不加载——skill 静默地压根没生效 | | 完整不打折的长上下文（单个 skill 10-50KB，还要加你的数据） | 中转普遍会截断、压缩或重新分块。而验证规则是散布在全文的，尾部一丢，诚信闸就跟着丢了 | | 真的是那个模型在接请求 | 部分转售会把「Claude」流量路由到别的或量化过的模型且不告知。你会拿到一份长得像 skill 输出、但完全没有自检行为的东西 | | 多步自检活过 system prompt | 会改写或前置自己 system prompt 的 harness，可能直接盖掉 skill 的验证指令 |
报障前请按这个顺序自查：
- 到底是谁在接你的请求？ 确认运行环境，以及请求真的打到了你以为的那个模型上——中间没有中转。
- skill 到底加载了没？ 让 agent 复述这个 skill 要求输出哪几节。复述不出来 = 文件压根没生效，这是加载问题不是 skill 问题。
- 跑一次留白测试。 喂一份缺关键数字的输入。正确行为是标出缺口（
❌未获取/ 「数据不足」）。如果它编了一个数字，说明诚信脚手架没在跑——不管它自称是什么模型。
这是单个维护者在自己能接触到的环境里的实测，不是对所有服务商的评测。第三方中转差异很大，有些可能没问题。要点只有一个：先把运行环境排除掉，因为「跑不通」的报障大多出在这儿。
参考型号（2026-08 核实，会过期）
| 层级 | 效果 | 当时的代表型号 | |------|------|--------------| | 推荐 | 完整执行，验证步骤生效 | Claude Opus 5 / Sonnet 5（及 Fable 5）、GPT-5.6 Sol / GPT-5.5 家族、Gemini 3.1 Pro | | 可用 | 结构完整，可能跳过验证 | DeepSeek V4、Qwen 3.5、GLM-5.2、Llama 4、Claude Haiku 4.5 | | 不建议 | 章节缺失，检查失效 | 小参数量模型（经验上 30B 以下） |
⚠️ 这张表按定义会过期——上一版停留在 2026-04 就已经落后两代。别照抄型号，用上面的能力门槛去测你手上的模型。各家型号请查官方文档：Anthropic · OpenAI · Google。
Claude 一栏来自 Anthropic 官方文档；其余为 2026-08 公开资料整理，各家发布节奏不同，以官方为准。
Changelog
v3.17 (2026-08-01) — three new skills: event marketing, crowdfunding, measurement
Three gaps closed, each found by auditing the library against outside industry material rather than by brainstorming. The recurring lesson: in all three, the most valuable part was not the framework — it was discovering that the source material's core premise had expired. Every fact below was verified against primary sources at build time; none was taken on the source's word.- New
brand-event-marketing— moment-driven marketing: the four event types by the question each answers (brand = who you are / product = what category you own / calendar = who you stand with / trend = where you're headed), a 7-step calendar-moment SOP and a 6-step trend-jacking SOP (four-plane trend read, competitor-axis reset, three value anchors, pyramid content matrix), plus a daily market-radar checklist. What the source lacked and this adds: the ambush-marketing legal gate. Newsjacking a big event is a legal act in the US — Olympic marks sit under the Ted Stevens Act with no consumer-confusion requirement (the USOPC has pursued "Olympian", "Team USA", even "Going for Gold"); FIFA's 2026 protection rests on the Lanham Act plus venue contracts and municipal clean zones rather than special legislation; and NFL enforcement is why every brand says "The Big Game". No rights, no reference — full stop. Also carries the practitioner's own measurement-honesty rule (PR counts page views not the outlet's monthly visits; creators count plays/likes/shares/comments not follower counts) and a no-hardcoded-event-dates freshness rule. Framework credited to its named speaker in the 参考方法论索引; the source's own self-reported campaign results were deliberately excluded — vendor self-reporting does not belong in a skill. - New
crowdfunding-launch— reward-based launch on Kickstarter / Indiegogo as four jobs at once (production capital, demand validation, launch awareness, distribution path), with a four-phase SOP and the post-campaign DTC flywheel. Opens with a delivery gate, because a reward campaign is an FTC-enforceable promise: the Chevalier case (the FTC's first Kickstarter action — $122k raised on a $35k goal, settled with a $111,793.71 judgment) and the iBackPack case ($800k+ across four campaigns on both platforms, nothing shipped) are in the file as the reason the rule is deliver or refund. 🔴 The stale premise caught here: Indiegogo retired Flexible Funding for new campaigns in October 2025 — both platforms are now all-or-nothing, so every "set a low goal and take the money via flexible" playbook (and every pre-2025 case screenshot showing a Flexible Goal) is teaching a route that no longer exists. Verified fee stack: Kickstarter 5% + 3% + $0.20 (small pledges 5% + $0.05, nothing at all if the goal is missed); Indiegogo 5% + ~3% + $0.30, InDemand now Late Pledge (5% native / 8% non-native). Includes a suitability gate that will tell a drop-ship or trading team its product is wrong for crowdfunding — "don't do this" is a designed output. - New
attribution-measurement— the measurement stack the library was missing: attribution, incrementality and MMM answer three different questions and get calibrated against each other rather than chosen between. Covers why summing independently-reported platform conversions always overcounts, geo-test design that starts from the minimum detectable effect that would actually change a budget decision (an under-powered test is worse than no test), open-source MMM selection between Google Meridian (Bayesian, Apache-2.0, now open to everyone, Scenario Planner added February 2026 for no-code budget modelling) and Meta Robyn (frequentist/ML, MIT, still maintained), and Bayesian calibration of MMM priors with incrementality results. 🔴 Two stale premises caught: the cookiepocalypse did not happen — Google reversed third-party-cookie deprecation in July 2024 and confirmed in April 2025 that Chrome would neither run the choice prompt nor deprecate, so any strategy whose立论 is "cookies are going away, therefore X" needs rewriting from the premise up (while not over-correcting: click-level tracking was never complete, which is the actual reason to run incrementality and MMM); and the rule-based attribution models are retiring on a live timeline — GA4 dropped first-click / linear / time-decay / position-based in November 2023, and Google Ads retired the same four in 2026 (unselectable for new conversion actions from mid-July, force-migrated to data-driven attribution by September), which means accounts still sitting on them should choose their migration rather than be migrated. - Validated against real data, not just linted. Each skill was dry-run on real cases, and each run found a defect that was then fixed:
brand-event-marketinghad no operational way to grade an event's 势能 (now a table of observable signals — search-trend shape in the target country, whether the platform runs an official window, discussion breadth — with "does my category actually connect to this moment" as a veto, not a bonus);crowdfunding-launchhad no way to size a pre-launch email list (now a back-solve from goal ÷ AOV ÷ list-conversion, and 🔴 the repo deliberately ships no default conversion rate — that number varies too much by category and list source for a borrowed one to be anything but precise and wrong).attribution-measurementwas run against a real multi-year DTC dataset (~30k orders, six years, GA4 + GSC + keyword data all connected) and correctly refused to produce a channel-ROI ranking: no ad-spend data at all meant two of the three legs could not run, order-source coverage sat at 16-30% every year, and first-touch matched last-touch on all but 6 of 3,964 orders — so the output was "the data cannot answer which channel works" plus the gap list, which is exactly what the honest-shortfall rule exists to produce. - Total: 58 skills across 13 chains (+3); standalone tools: 7.
v3.16 (2026-08-01) — the honesty-scaffolding sweep: reproducible scoring, honest quotas, evidence gates
Closes the two design questions parked in v3.15, then follows the thread: a gap found in one skill turned out to be a class of gap, so all 62 skills were swept for it. The pattern throughout — the repo's honesty scaffolding existed in its vocabulary but was missing at specific spots, and the spots it was missing were often the highest-consequence ones. No skills added or removed.- Output quotas are now targets, not pass marks (#16).
brand-market-scan,brand-track-hypothesisandsocial-content-calendardemanded "≥8 rows + ≥3 user quotes per layer", "≥10 features", "exactly 5 strategic clues, each with ≥3 lines of numeric support" — and when the data isn't there, a quota rewards filling the shape, which fights the same file's data-acquisition ledger and 推测值-labeling rules. Every quota is kept, but reframed with an honest shortfall path: state what's missing, why, and what it costs the conclusion; log it in the existing ledger; and — the load-bearing sentence — an honest shortfall counts as completing the step. Also separates 必出的章节 from 必出的结论: always emit the section, never force a conclusion the evidence won't carry. - New
docs/scoring-conventions.md— the mechanical half of reproducible scoring (#15). The repo runs four different scales (/100, 1-5 stars, six dims /30, 100-pt bands); differing scales are fine, but the same input should produce the same ranking twice. Scale-agnostic spec covering anchor-pair normalization with clamping (logfor heavy-tailed inputs like BSR, reverse anchors for lower-is-better), missing values, tie-breaking, showing the arithmetic per dimension, and not manufacturing precision the inputs don't have. Wired into all four weighted-scoring skills (amazon-product-selection,amazon-product-shortlist,affiliate-readiness-audit,affiliate-program-ops).
本分基于 N/M 维. A 90 from 3 dimensions and a 90 from 5 mean different things, and hiding the count dresses "incomplete data" up as "scores well". Above 40% missing weight, no total is produced at all.
- Deliberately not done: universal anchor values. "月搜索 >10,000 = 高分" is not the same claim in pet supplies as in consumer electronics. Existing thresholds are preserved verbatim as calibrate-me defaults that the skill must print; inventing universal ones would replace honest vagueness with false precision — the same disease #16 treats, pointed the other way.
amazon-listing-copywriter— title and Item Highlights now ship as 3 candidate versions (#21). Three strategy-distinct candidates each (volume-first / long-tail-first / scenario-first) with per-version character counts, closing with one recommended version whose rationale must state what traffic it prioritizes, what it gives up, and the condition under which to switch — a bare "use version A" is defined as not completing the step. All three versions are character-counted and compliance-checked, not only the recommended one: operators pick a non-recommended version routinely, and that previously meant shipping a string nothing had validated against the ≤75 limit, the same-word ≤2 rule, or the banned-character set.- The Item Highlights output section was missing entirely. The field is named in the skill's frontmatter and four times in its rules — "migrate long-tail and scenario words into Item Highlights" — but the report template went straight from title to bullet points. The skill had been telling the model to use a field it never asked it to write, ever since the 2026-07-27 policy created it. Section added and marked non-empty-able, since it is where everything the 75-char title had to drop now lands.
实际 2/3 版，原因：无第二人群词) completes the step, and a 多版本真实性 row was added to the compliance self-check table.
- Lint gained
/docs/and a wider negation vocabulary.docs/joins the support-file exemption (it holds conventions, not skills), and the deception check's negation list grew (严禁不得避免不披露等于把) after it false-positived on lines warning against disguising data gaps. Re-tested: genuine伪装成订单更新phrasing is still caught. amazon-ip-risk-assessmenthad no rule against inventing a patent number. Found by sweeping the other 13 Amazon skills for the same defect class. It is the one skill in the chain that emits legally consequential identifiers — patent numbers, trademark registration numbers, holders, legal status — that a seller acts on with tooling money; its 数据验证（必做）held four items, all about telling things apart correctly (Assignee vs Security Interest vs Licensee, ® vs ™, Active vs Expired), and none about whether the record exists at all. Meanwhileamazon-keyword-researchforbids inventing a competitor keyword andbrand-market-scanforbids filling in an ASIN from memory. The lowest-stakes outputs in the repo were guarded and the highest-stakes one was not. Added: a top-of-file 🔴 evidence rule, a search-execution record (platforms reached, exact queries, date, success flag) that precedes the result tables, ✅/⚠️/❌ retrieval status + source + date columns on both the patent and trademark tables, "no hits" stated as an explicitly valid result, and — the load-bearing gate — if the search did not execute, the tables are marked ❌未获取 and the overall risk grade is withheld rather than guessed from category intuition. A fabricatedUS D123,456 — Active — held by Xreads exactly like a real one, and the seller has no way to tell.amazon-ad-diagnosisflagged compliant bullets as too long. Its listing-diagnosis table carried每条 ≤200 字符as a 健康标准 with 问题信号 "超长 → 精简" — an unsourced number that contradicts the official 2024-08-15 policy of 10–255 characters, whichamazon-compliance-review,amazon-listing-copywriter,amazon-keyword-research,amazon-market-researchandamazon-pre-launch-reviewall carry correctly. The diagnosis would have told sellers to cut copy that was never over the limit. Corrected, with the ~1000-character total kept as a labeled soft budget that is explicitly not a diagnostic finding — the same de-mythologizing v3.14 applied everywhere else in the chain.- Swept and clean across all 14 Amazon skills: section numbering sequential, every
/amazon-*cross-reference resolves, the 2026-07-27 dual-field policy reached both gates (compliance-reviewandpre-launch-reviewdo check Item Highlights — the copywriter gap above was isolated, not systemic), 数据验证（必做）present 14/14, #20's scoring conventions genuinely wired into both scoring skills (anchors printed,本分基于 N/5 维, >40% circuit breaker), no fabricated ranking-weight percentages, and image specs consistent at ≥1600×1600. trustpilot-voc-quickhad no "couldn't fetch" path — and a worked numeric example to copy from. Its output template specified rating, review count, star distribution, high-frequency words and a trend call, with nothing tying any of them to the fetch actually succeeding; the fallback section listed what to try (Google cache, ask the user to paste) but never what not to do when all of it fails. Trustpilot throttles hard, so failure is the common path, not the edge case. Worse, the file shipped a fully-worked real example (4.5/5, 269 reviews, 216/21/4/2/26) — a model that couldn't fetch had a plausible numeric template sitting in its own prompt. Added a 🔴 data-acquisition rule binding every figure to the fetch, an explicit ❌未获取 output block, partial-retrieval handling (don't back-infer a distribution from an overall rating), failed brands kept as ❌ rows in comparison tables instead of filled in, and the example rewritten to placeholders with a warning not to transplant any number from it. The sharp edge: this skill feedsbrand-market-scan第零步, the very report #16 hardened with "严禁用记忆或推断补齐…评论原文" — the consumer was guarded and its producer was not.media-press-discoveryhanded over pattern-guessed emails for real, named journalists with no send gate.guessemails.pyguessesfirstname.lastname@outlet.comfrom a name and a domain; the schema even carried anemailverifiedcolumn — but the quick-start ran the guesser without the--verifyflag the script already supports, and nothing anywhere said don't send tounverified. A wrong guess bounces (degrading your sending domain), reaches a different real person at that outlet, or hits a spam trap. Sibling skills already knew this —linkedin-prospectingsays pattern-guess → verify with NeverBounce/ZeroBounce, andcontact-extractorships confidence tiering plus--verify. Fixed:--verifypromoted into both quick-start flows with its real ~60% SMTP-probe accuracy stated,emailverifieddocumented as a hard gate rather than a note (smtpfail= unproven, not disproven → manual queue, don't drop the journalist), a ban on reconstructing any journalist name / byline / URL / date from memory, and the CAN-SPAM / GDPR identify-yourself line the repo already enforces elsewhere.reddit-voc's Top 5 quotas never got the #16 treatment. "核心痛点 Top 5（带原话引用）" and "竞品翻车点 Top 5" are hard quotas in a skill whose entire value is verbatim user language — and Reddit now rate-limits and 403s routinely, so shortfall is normal. The skill's existing "永远抄用户原话，别自己改写" guards against paraphrase, not against invention, and quoting presupposes having something to quote. Added the standard shortfall path (3 real pain points → write 3 and label实际 3/5+ reason), a ban on fabricating quotes, usernames, permalinks or upvote counts, an outright ❌未获取 report when scraping fails, and the 必出的章节 / 必出的结论 split. Also stopped the ≥2-community cross-check from becoming its own quota: single-community insights get labeled, not padded out with a lookalike.- Swept and clean across the other 45:
finance/(8) uses a different but real convention — source table + confidence + timestamp + "须核实一手源" + disclaimer, withfinance-tax-nexus-vat-diagnosticciting Council Reg (EU) 2026/382, HMRC and Wayfair and flagging its own 2026 moving targets ·trustpilot-voc-deepcarries a sample-size + credibility table and runs a real local scraper ·linkedin-prospectingandcontact-extractoralready gate on verification ·serp-content-teardownis deterministic by design (no LLM) ·tools/(7) are deterministic scripts ·report-pdf-exportandbrand-chart-visualizeemit no facts. Structure clean repo-wide: section numbering, cross-references and count claims all check out. - Total: 55 skills across 13 chains (unchanged); standalone tools: 7.
v3.15 (2026-07-26) — repo hygiene: dedupe, red-line fix, frontmatter, CI
No skills added or removed; this release fixes structural debt found in an external audit, with each claim verified against the tree first.- Deleted 14 stale duplicate skills (−2,704 lines). Every Amazon skill existed twice —
amazon/<name>.mdandamazon/<category>/<name>.md— and all 14 pairs had diverged. The root copies are canonical (README links only to them, and they carry two sections the subdirectory copies had lost); the subdirectory copies were referenced nowhere. Left in place, an agent grepping the repo
README truncated. View on GitHub
