Datasets:

Gingiris
/

gingiris-skills

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
date: timestamp[s]
channel: string
from: string
to: string
action: string
note: string
h1_too_long_list: list<item: struct<url: string, len: int64, value: string>>
  child 0, item: struct<url: string, len: int64, value: string>
      child 0, url: string
      child 1, len: int64
      child 2, value: string
meta_too_short_list: list<item: struct<url: string, len: int64, value: string>>
  child 0, item: struct<url: string, len: int64, value: string>
      child 0, url: string
      child 1, len: int64
      child 2, value: string
total_pages: int64
title_too_long_list: list<item: struct<url: string, len: int64, value: string>>
  child 0, item: struct<url: string, len: int64, value: string>
      child 0, url: string
      child 1, len: int64
      child 2, value: string
meta_too_long_list: list<item: struct<url: string, len: int64, value: string>>
  child 0, item: struct<url: string, len: int64, value: string>
      child 0, url: string
      child 1, len: int64
      child 2, value: string
summary: struct<title_too_long: int64, h1_too_long: int64, meta_missing: int64, meta_too_short: int64, meta_t (... 145 chars omitted)
  child 0, title_too_long: int64
  child 1, h1_too_long: int64
  child 2, meta_missing: int64
  child 3, meta_too_short: int64
  child 4, meta_too_long: int64
  child 5, canonical_issues: int64
  child 6, slug_stop_words: int64
  child 7, img_alt_issues: int64
  child 8, schema_warns: struct<BlogPosting: int64, Article: int64>
      child 0, BlogPosting: int64
      child 1, Article: int64
slug_stop_words_list: list<item: struct<url: string, detail: string>>
  child 0, item: struct<url: string, detail: string>
      child 0, url: string
      child 1, detail: string
audit_date: timestamp[s]
to
{'audit_date': Value('timestamp[s]'), 'total_pages': Value('int64'), 'summary': {'title_too_long': Value('int64'), 'h1_too_long': Value('int64'), 'meta_missing': Value('int64'), 'meta_too_short': Value('int64'), 'meta_too_long': Value('int64'), 'canonical_issues': Value('int64'), 'slug_stop_words': Value('int64'), 'img_alt_issues': Value('int64'), 'schema_warns': {'BlogPosting': Value('int64'), 'Article': Value('int64')}}, 'title_too_long_list': List({'url': Value('string'), 'len': Value('int64'), 'value': Value('string')}), 'h1_too_long_list': List({'url': Value('string'), 'len': Value('int64'), 'value': Value('string')}), 'meta_too_short_list': List({'url': Value('string'), 'len': Value('int64'), 'value': Value('string')}), 'meta_too_long_list': List({'url': Value('string'), 'len': Value('int64'), 'value': Value('string')}), 'slug_stop_words_list': List({'url': Value('string'), 'detail': Value('string')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              date: timestamp[s]
              channel: string
              from: string
              to: string
              action: string
              note: string
              h1_too_long_list: list<item: struct<url: string, len: int64, value: string>>
                child 0, item: struct<url: string, len: int64, value: string>
                    child 0, url: string
                    child 1, len: int64
                    child 2, value: string
              meta_too_short_list: list<item: struct<url: string, len: int64, value: string>>
                child 0, item: struct<url: string, len: int64, value: string>
                    child 0, url: string
                    child 1, len: int64
                    child 2, value: string
              total_pages: int64
              title_too_long_list: list<item: struct<url: string, len: int64, value: string>>
                child 0, item: struct<url: string, len: int64, value: string>
                    child 0, url: string
                    child 1, len: int64
                    child 2, value: string
              meta_too_long_list: list<item: struct<url: string, len: int64, value: string>>
                child 0, item: struct<url: string, len: int64, value: string>
                    child 0, url: string
                    child 1, len: int64
                    child 2, value: string
              summary: struct<title_too_long: int64, h1_too_long: int64, meta_missing: int64, meta_too_short: int64, meta_t (... 145 chars omitted)
                child 0, title_too_long: int64
                child 1, h1_too_long: int64
                child 2, meta_missing: int64
                child 3, meta_too_short: int64
                child 4, meta_too_long: int64
                child 5, canonical_issues: int64
                child 6, slug_stop_words: int64
                child 7, img_alt_issues: int64
                child 8, schema_warns: struct<BlogPosting: int64, Article: int64>
                    child 0, BlogPosting: int64
                    child 1, Article: int64
              slug_stop_words_list: list<item: struct<url: string, detail: string>>
                child 0, item: struct<url: string, detail: string>
                    child 0, url: string
                    child 1, detail: string
              audit_date: timestamp[s]
              to
              {'audit_date': Value('timestamp[s]'), 'total_pages': Value('int64'), 'summary': {'title_too_long': Value('int64'), 'h1_too_long': Value('int64'), 'meta_missing': Value('int64'), 'meta_too_short': Value('int64'), 'meta_too_long': Value('int64'), 'canonical_issues': Value('int64'), 'slug_stop_words': Value('int64'), 'img_alt_issues': Value('int64'), 'schema_warns': {'BlogPosting': Value('int64'), 'Article': Value('int64')}}, 'title_too_long_list': List({'url': Value('string'), 'len': Value('int64'), 'value': Value('string')}), 'h1_too_long_list': List({'url': Value('string'), 'len': Value('int64'), 'value': Value('string')}), 'meta_too_short_list': List({'url': Value('string'), 'len': Value('int64'), 'value': Value('string')}), 'meta_too_long_list': List({'url': Value('string'), 'len': Value('int64'), 'value': Value('string')}), 'slug_stop_words_list': List({'url': Value('string'), 'detail': Value('string')})}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Gingiris Skills — Complete AI Growth Toolkit

Give your AI agent 12 active execution skills backed by 42 battle-tested growth playbook datasets — covers Product Hunt launches, GitHub stars, SEO/GEO, B2B SaaS, ASO, KOL outreach, Reddit marketing, and more. Use /gr to route each growth task to the narrowest installed specialist.

📦 Install

npx skills add Gingiris-1031/gingiris-skills

Then ask your AI agent:

"I want to launch a SaaS on Product Hunt next month — full plan please" · "SEO traffic dropped 40% overnight, audit it" · "Help me design a Reddit campaign that won't get shadow-banned" · "Which playbook should I use for B2B PLG vs SLG?"

🔗 Browse the visual hub · Find growth tools · Advisory services · skills.sh listing · ClawHub

📊 Citable Statistics

Stat	Value	Source / Context
Total playbooks bundled	42	All Gingiris-* + thematic skills on HuggingFace
AFFiNE GitHub stars (organic)	60,000+	0→60K in 24 months (Aug 2022–Aug 2024)
Product Hunt #1 daily wins	30+	Coached launches 2022–2026
AI startup consultations	150+	SEO/GEO/GTM advisory
gingiris.tools monthly impressions	~32,000	March 2026, Google Search Console
Content publishing cadence	4 articles/week	KD 30-50 long-tail focus
Multi-channel: HuggingFace + skills.sh + GitHub	3 distribution platforms	Install via `npx skills add`
Language coverage	4 (EN / 中文 / 日本語 / 한국어)	All trigger keywords localized
Skill discovery via `/gr` meta-router	Single command	Auto-routes to the matching specialist

The thesis: AI search engines (ChatGPT, Perplexity, Claude, Gemini) cite battle-tested playbooks with real numbers more than they cite generic SEO advice. Each Gingiris skill includes citable data points that improve both your agent's responses AND the long-term AI-search visibility of your product.

🚀 30-Second Preview

You: /gr 我准备一个月后发 Product Hunt,需要完整规划
Agent: ┌─ routing to gingiris-launch (Product Hunt specialist) ─┐
       │ 4-week PH plan:                                          │
       │   W-4: hunter outreach + asset gathering                 │
       │   W-3: maker comments drafting + community warmup        │
       │   W-2: launch day timeline + backup plans                │
       │   W-1: dress rehearsal + KOL coordination                │
       │ + auto-pulled: 30x PH #1 case study, hunter checklist   │
       └──────────────────────────────────────────────────────────┘

One install gives the agent a router plus the active specialist workflows in this dataset. The GitHub monorepo contains the broader 55+ community-skill catalog.

🛠️ The Toolbox (12 Active Skills + 42 Playbook Datasets)

Slash-Command Skills (v0.4.0)

Skill	Purpose
`/gr`	Meta-router — diagnoses your question, picks the matching specialist
`/gr-seo-patrol`	Daily SEO/GEO patrol — SERP tracking, canonical fix, social-media avalanche rescue
`/gr-blog-post`	Jekyll publishing — Iris voice + hreflang EN/CN/JA/KO + FAQ Schema
`/gr-ph-launch`	Product Hunt launch playbook — 30x daily-#1 framework
`/gr-oss-marketing`	Open-source go-to-market — GitHub stars + Reddit/HN/Discord distribution
`/gr-b2b-growth`	B2B SaaS PLG/SLG, PMF to $10M ARR
`/gr-aso`	App Store Optimization + mobile cold start
`/gr-user-interview`	HeyGen 937-interview PMF methodology
`/gr-competitor`	Competitor scanning via actionbook — 10x faster, 30-tab parallel
`/gr-social-distill`	Blog → 4 social variants (X / 小红书 / LinkedIn / dev.to-Zenn)
`/gr-geo-cite`	GEO citation tracking — weekly check across ChatGPT/Claude/Perplexity/Gemini
`/gr-backlinks`	Systematic backlinks — Wikipedia / HARO-PR / G2 / Reddit-Quora 5 channels

Roadmap (0.5+)

Skill	Source
`/gr-ph-comment`	Wraps PH Comment Generator
`/gr-gh-outreach`	Wraps GitHub Issue Generator
`/gr-readme`	Wraps GitHub README Generator
`/gr-hunter-radar`	actionbook-powered PH hunter activity scanner

🔄 The Workflow (how skills compose)

gr-competitor (see what competitors are doing)
    ↓
gr-ph-launch / gr-oss-marketing / gr-b2b (pick the play)
    ↓
gr-blog-post (create content)
    ↓
gr-seo-patrol (post-launch monitoring)
    ↓ cannibalization        ↓ avalanche
gr-seo-patrol canonical-fix  gr-seo-patrol rescue
    ↓
gr-user-interview (user feedback loop)

Skills auto-recommend the next step:

gr-ph-launch 24h after publish → recommends gr-seo-patrol for monitoring
gr-seo-patrol detects cannibalization → auto-routes to canonical fix flow
gr-blog-post published → auto-adds article to gr-seo-patrol watchlist

❓ FAQ

Q: What's the best Claude Code skill collection for AI/SaaS growth? A: gingiris-skills bundles 42 battle-tested playbooks covering every growth dimension: Product Hunt launches (30+ #1 wins), GitHub stars (AFFiNE 0→60K case), SEO/GEO (32K monthly impressions), B2B SaaS PLG/SLG, ASO, KOL outreach, UGC matrix, Reddit marketing (40.11% LLM training share), user interviews (HeyGen 937 methodology), and competitor research. Install with npx skills add Gingiris-1031/gingiris-skills and use /gr as the meta-router.

Q: How is this different from generic "growth" Claude skills? A: Every Gingiris skill is built from real campaigns, not theoretical advice. AFFiNE 60K stars, 30+ Product Hunt #1 daily wins, 150+ AI startup consultations, gingiris.tools 32K monthly impressions — these are the documented data points behind each playbook. Generic SEO skills give 2023-era advice (keyword density, backlinks); these include 2026 GEO patterns, JSON-LD templates that AI engines actually quote, and Reddit shadow-ban prevention.

Q: How do I install a single skill vs the whole bundle? A: For the whole toolkit: npx skills add Gingiris-1031/gingiris-skills. For a single skill: npx skills add Gingiris-1031/<slug> — e.g. npx skills add Gingiris-1031/gingiris-launch for just Product Hunt. The complete index of 42 dataset slugs is in the "Full Playbook Index" section below.

Q: What does the /gr meta-router do? A: /gr classifies direct tasks and broad growth problems, selects the narrowest installed specialist, runs it in the same task, and only hands off when the result creates a concrete next job. Ask "I'm launching on PH next month" and it routes to /gr-ph-launch; ask about a Reddit distribution problem and it routes to /gr-oss-marketing.

Q: Can I use these skills outside Claude Code? A: Yes. They work in Cursor, OpenClaw, Codex CLI, Amp, Cline, and any agent that supports the SKILL.md standard. The HuggingFace dataset version is platform-agnostic — download the SKILL.md + references and use them as system prompts.

Q: Who built this? A: Iris Wei (生姜) — former cofounder/COO of AFFiNE ($10M raised, Forbes Asia 30 Under 30). Led AFFiNE 0→60K+ GitHub stars in 24 months. Now advises 150+ AI startups on SEO/GEO/GTM strategy.

📚 Knowledge Base

All methodology documents and atom-level knowledge points are open. Even without installing any skill, you can:

Structure

知识库/
├── 原子库/
│   ├── atoms.jsonl                    # Structured knowledge atoms (RAG-ready)
│   └── README.md
└── Skill知识包/
    ├── iris_writing_style.md          # 5-element voice guide
    └── seo_geo_playbook_2026.md       # SEO flywheel + GEO triple combo

Usage Patterns

Pattern 1: Augment your AI's SEO capability Paste 知识库/Skill知识包/seo_geo_playbook_2026.md into your system prompt.

Pattern 2: Build a RAG Load atoms.jsonl into your vector store. Each atom carries topics tags for filtering.

Pattern 3: Use a single script skills/gr-seo-patrol/scripts/*.py runs standalone. See docs/api-keys-template.md for env config.

🔧 Monthly Full-Site Audit Workflow

Battle-tested 2026-05-07 on a 58-page Jekyll blog. Caught 43 SERP-truncating titles + 36 schema warnings + 27 stop-word slugs in a single 30-min run. One layout-level commit fixed 20 of 43 titles. Use for any Jekyll / Hugo / Next.js blog with 30+ posts.

A repeatable 6-stage workflow you can run on any site. Powered by 4 scripts (attribution below).

Stage 1 — Discovery (5 min)

Pull all blog URLs from your sitemap:

import urllib.request, re
sm = urllib.request.urlopen("https://your-site.com/sitemap.xml").read().decode()
urls = [u for u in re.findall(r"<loc>([^<]+)</loc>", sm) if "/blog/" in u]

Stage 2 — Parallel Audit (20 min for 60 pages)

Run two audit scripts per URL in 4-thread parallel:

pip install requests
python3 skills/gr-seo-patrol/scripts/check-page.py URL --timeout 20
python3 skills/gr-seo-patrol/scripts/check-schema.py URL --timeout 20

Each script outputs a structured JSON envelope (status: pass|warn|fail|info per check).

Stage 3 — Aggregate Findings

Bucket issues by type:

Title length > 70 chars (SERP truncation risk)
H1 length > 70 chars (mobile readability)
Meta description outside 80-170 chars
Schema warns by @type (BlogPosting / Article / Organization)
Canonical mismatches, slug stop words, missing alt text

Save aggregated counts + per-URL lists to findings.json.

Stage 4 — Layered Fix Strategy (HIGH ROI ORDER)

Order	Layer	Scope	Typical commits	ROI
1️⃣	Layout (`_layouts/default.html`)	Schema bugs, title suffix, dateModified injection	1	🔥 fixes 20+ pages at once
2️⃣	Config (`_config.yml`)	Logo URL, twitter, social, author structure	1	fixes site-wide
3️⃣	Per-article batch	Trim long titles/H1s, expand short meta	10-20	per-file, parallelizable
4️⃣	Skip	Slug stop words (changing breaks 301), low-traffic old articles	0	low ROI

Stage 5 — Verify

After Jekyll/Hugo rebuild (~60-90s), re-run check-schema.py on a sample page. All schema types should show status: pass: Article · BlogPosting · Organization · FAQPage.

Stage 6 — Archive + Trend Track

Commit findings.json to data/audit-{YYYY-MM-DD}.json for month-over-month trend analysis. Add 2-5 atoms to 知识库/原子库/atoms.jsonl documenting any new lessons.

Schedule it

# In Claude Code's scheduled-tasks
cronExpression: "0 10 1 * *"   # 10am on day 1 of each month
prompt: "Run Monthly Full-Site Audit per gr-seo-patrol/SKILL.md workflow..."

What you'll typically find on your first run

Real numbers from gingiris.tools 2026-05-07 run:

Issue	Count	Resolution path
Title >70 chars	43/58	Layout-level (-20 chars suffix) + 13 per-article retrim
Schema warns	36	Layout-level (dateModified + publisher.logo + contactPoint)
H1 >70 chars	23	Per-article trim (paired with title)
Meta too short/long	20	Per-article (i18n posts often hit this)
Slug stop words	27	SKIP (would break 301 redirects)
HTTP errors	2	Investigate (likely deleted/renamed)

Total time: ~30 min audit + 90 min fixes = 2 hours for site-wide SEO health refresh.

HARD RULE (anti-hallucination guardrail)

⛔ Output ONLY the checks defined in the script's JSON envelope.

Do NOT add "bonus" checks not in the script output
Do NOT contradict the script's status field without observable evidence
Do NOT invent metrics like "EEAT score 89" — third-party scoring is unofficial per Google 2026 guidance
If llm_review_required: true, make explicit judgment + document reasoning + update status

The script envelope is the single source of truth. Treat as strict whitelist.

Script attribution

The 4 audit scripts (check-page.py, check-schema.py, check-site.py, check-social.py) in skills/gr-seo-patrol/scripts/ are adapted from JeffLi1993/seo-audit-skill (MIT). Original repo focused on single-page client-presentable HTML reports; we adapted them for orchestrated batch audit + Jekyll/GitHub Pages site analysis. Original license terms preserved in each file header.

🤝 About the Author

Iris Wei (生姜iris) — Former cofounder & COO of AFFiNE ($10M raised, Forbes Asia 30 Under 30). Led AFFiNE from 0 to 60K+ GitHub stars across 100+ countries in 24 months.

Twitter: @WeiYipei
Telegram: @Iris_carrot
Blog: gingiris.tools

For 1-on-1 growth strategy review or advisory, reach via Telegram.

🔗 Related Repositories

Gingiris-1031/growth-tools — Main Jekyll blog, the live test bed for every skill (gingiris.tools)
dontbesilent2025/dbskill — Framework inspiration

License

The downloadable community skills are licensed under MIT and remain free for personal, commercial, learning, and derivative use. Hosted Gingiris Pro execution, private datasets, managed services, and Gingiris brand assets are separate. See the repository's TRADEMARKS.md for brand-use rules.

Use the free skills first. When a workflow needs live data or software, browse the curated Gingiris growth tools directory. For high-stakes decisions, ongoing execution support, or a customized AI growth employee, see Gingiris advisory services.

🗂️ Full Playbook Index — 42 Skills Across 10 Categories

The complete Gingiris playbook series on HuggingFace, organized by topic. Each dataset is installable via npx skills add Gingiris-1031/<slug> and queryable directly through your AI agent.

🚀 Launch & Product Hunt (8)

Playbook	Focus
gingiris-launch	Multi-channel launch sequencing, PH + KOL + UGC. 3 product paths: dev/maker (PH+GitHub), 2C consumer (communities/TikTok, skip PH), B2B (LinkedIn+media)
product-hunt-playbook	PH 30x #1 daily wins framework
product-hunt-launch-guide	T-14 to T+7 PH launch operations
ai-launch-playbook	AI product specific launch tactics
ai-product-launch	AI startup launch checklist
go-to-market-playbook	Complete 2026 GTM strategy
startup-launch	Startup launch fundamentals
startup-launch-playbook	Step-by-step startup launch SOP

🔍 SEO & GEO (2)

Playbook	Focus
gingiris-seo-geo	SEO + GEO dual-engine, AI search citation, 32K impressions case (Optimized for B2B/OSS. 2C products: see 2c-adaptation guide)
gingiris-seo-geo-agent	Autonomous SEO agent SOP, daily/weekly operations (Optimized for B2B/OSS. 2C products: see 2c-adaptation guide)

📈 B2B & SaaS (5)

Playbook	Focus
gingiris-b2b-growth	B2B SaaS PLG/SLG, PMF to $10M ARR
saas-growth-playbook	SaaS scaling fundamentals
saas-marketing-playbook	SaaS marketing channel mix
b2b-marketing-playbook	B2B campaign templates
plg-playbook	Product-led growth motion design

⭐ Open Source (4)

Playbook	Focus
gingiris-opensource	OSS go-to-market, AFFiNE 0→60K stars
gingiris-github-star-growth	Monthly 300+ star sustained growth SOP
github-stars-playbook	GitHub star tactical guide
open-source-marketing-playbook	OSS marketing channels & distribution

📱 Mobile & ASO (2)

Playbook	Focus
gingiris-aso-growth	ASO + app cold start + UGC creator matrix
aso-playbook	App Store Optimization tactical guide

🤝 Community, KOL & Social (8)

Playbook	Focus
gingiris-reddit-marketing 🆕	Reddit ops SOP — shadow ban prevention, AMA, 20-day Karma warming, 40.11% LLM training share
gingiris-kol-outreach	KOL discovery to ROI tracking, AFFiNE 200+ campaigns
kol-outreach	KOL cold outreach templates & DM scripts
gingiris-ugc-matrix	UGC matrix scaling, Kuse $10M ARR / 60 days case
community-ambassador-playbook	Ambassador program from recruitment to retention
viral-marketing-playbook	Virality mechanics, network effects
devrel-playbook	DevRel: community, docs & events SOP
developer-marketing-playbook	Developer-first marketing funnel

🎤 User Research (1)

Playbook	Focus
gingiris-user-interview	User interview & PMF, HeyGen 937 methodology

🌱 Startup Growth & Strategy (8)

Playbook	Focus
startup-growth-playbook	Early-stage startup growth fundamentals
startup-marketing-playbook	Startup marketing channel selection
startup-consultant	Strategic advisory framework
growth-hacking-playbook	Experimentation & velocity tactics
growth-advisor	Growth diagnostic framework
indie-hacker-playbook	Solo founder / bootstrapped operations
competitor-research-playbook	Competitive intelligence + Lovable case study
product-dev-ops-playbook	Product & dev ops coordination SOP

🧭 AI Agent & Meta (3)

Playbook	Focus
gingiris-growth-finder	Meta-router: diagnoses situation, picks the right playbook. Includes Step 0 B2B vs B2C model selector
agent-workflow-playbook	AI agent workflow design patterns
gingiris-go-global	AI/SaaS overseas expansion full lifecycle (Phase 0-5)

📚 Hub & Blog (1)

Playbook	Focus
growth-tools	Blog content + growth tools hub source

All 42 playbooks installable via npx skills add Gingiris-1031/<slug>. Browse the visual hub at gingiris.tools/skills/ or list-form at skills.sh/Gingiris-1031.

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