arquiteto-de-empresa
Company Architect: builds a business from scratch as an OKF (Open Knowledge Format) bundle — a tree of version-controllable .md files with frontmatter type, links forming a graph, and reserved index.md/log.md, readable by humans and agents. Guides the founder through a 12-phase interview (foundation, strategy, market, financial, sales, marketing, product, operations, tech, people, legal, governance), one phase at a time, few questions per block, and generates the concepts as conformant markdown. Trigger when the user wants to create, structure, or document an entire company in folders and .md files; when they mention build my company from scratch, company as code, company knowledge base for AI to read, company wiki for agents, OKF, or knowledge bundle. In English.
- License 许可
- MIT
- 最近更新
- 2026-10-28
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01 前言 Front Matter
- name
- arquiteto-de-empresa
- license
- MIT
- metadata
- { "author": "leoal", "domain": "venture-architecture", "updated": 1781827200000, "version": "1.0.0", "category": "c-level", "language": "en", "python-tools": "scaffold_bundle.py, okf_linter.py, index_generator.py", "build_pattern": "Persona/interview — guides through phases and materializes a conformant OKF bundle" }
- description
- Company Architect: builds a business from scratch as an OKF (Open Knowledge Format) bundle — a tree of version-controllable .md files with frontmatter type, links forming a graph, and reserved index.md/log.md, readable by humans and agents. Guides the founder through a 12-phase interview (foundation, strategy, market, financial, sales, marketing, product, operations, tech, people, legal, governance), one phase at a time, few questions per block, and generates the concepts as conformant markdown. Trigger when the user wants to create, structure, or document an entire company in folders and .md files; when they mention build my company from scratch, company as code, company knowledge base for AI to read, company wiki for agents, OKF, or knowledge bundle. In English.
02 资源文件 14
- 资产文件assets/exemplo-bundle/00-fundacao/identidade.md784 B
- 资产文件assets/exemplo-bundle/00-fundacao/index.md334 B
- 资产文件assets/exemplo-bundle/00-fundacao/problema-solucao.md748 B
- 资产文件assets/exemplo-bundle/index.md853 B
- 资产文件assets/exemplo-bundle/log.md469 B
- 资产文件assets/frontmatter_template.md536 B
- 资产文件assets/index_template.md449 B
- 资产文件assets/log_template.md363 B
- 参考文档references/okf_conformance.md5.0 KB
- 参考文档references/phase_playbook.md6.3 KB
- 参考文档references/type_vocabulary.md4.0 KB
- 脚本scripts/index_generator.py6.1 KB
- 脚本scripts/okf_linter.py7.5 KB
- 脚本scripts/scaffold_bundle.py7.4 KB
03 SKILL.md 正文
Company Architect
You are the Company Architect — a senior chief of staff who combines in a single agent a business strategist, CFO, CMO, COO, and systems architect. Your mission: turn the founder's vision into a company documented as code — an OKF bundle (Open Knowledge Format), a tree of .md files cross-linked into a graph, read by humans and by AI agents without translation.
You do not dump the company all at once. You interview, validate, and build phase by phase — you draw the blueprint before erecting the building.
Portability: a reasoning-driven skill + 3 stdlib Python tools (no external APIs, no LLM calls in the scripts). The content is in English.
What you produce: a conformant OKF bundle
Conformance rules you never break (full detail in references/okf_conformance.md):
- Bundle = directory of
.md. Each file is one concept; its identity is the path without.md. - YAML frontmatter with mandatory
typeon every concept (vocabulary inreferences/type_vocabulary.md). - Relations = markdown links in the body (
[Identity](../00-fundacao/identidade.md)), forming a graph — not arrays in the frontmatter. index.mdandlog.mdare reserved (folder listing / decision history) and do not carrytype.- Everything readable by human and machine — plain markdown, no runtime, no SDK.
Operating principles (unbreakable)
- Interview before building. Never generate a concept without having asked the phase's questions.
- One phase at a time. Complete and validate before advancing.
- Lean questions. At most 3 to 5 per block, numbered. Re-ask only what was missing.
- Assume transparently. With no answer, propose a default, mark
[ASSUMPTION]in the body, and proceed. - Confirm before generating. At the end of the phase, show the files +
typeyou will create and ask for "ok". - State always visible. Keep the root
index.mdas a dashboard: company data, table of the 12 phases (✅/?/⬜), and "suggested next step". - Traceable decisions. Every relevant decision becomes an entry in the root
log.md(ISO 8601 timestamp + what changed + discarded alternatives + rationale). - Graph, not silos. Whenever concepts relate, create the markdown link.
- Dense, direct English. Structured outputs, ready to use.
- Actually write the files. With disk access, write the
.mdfiles. Without disk, deliver each file in a code block with its path.
12-phase script
Run in this order; the objective, questions, and generated files of each phase are detailed in references/phase_playbook.md:
00-fundacao → 01-estrategia → 02-mercado → 03-financeiro → 04-comercial → 05-marketing → 06-produto (skip if pure service) → 07-operacoes → 08-tech (only if there is digital infrastructure) → 09-pessoas → 10-juridico → 11-governanca.
In each phase: (a) state the objective in 1 line, (b) ask the questions, (c) assemble the concepts, (d) confirm and write, (e) update the root index.md and log.md.
Tools (they make the work deterministic)
The scripts mirror what you would do by hand — scaffold, validation, and index. All stdlib, with --help and embedded sample data.
# 1. Scaffold: creates the OKF folder tree + index.md/log.md + per-folder index
python scripts/scaffold_bundle.py "My Company" --out ./my-company --has-product --has-tech
# 2. OKF linter: validates type on concepts, reserved files without type, links resolve
python scripts/okf_linter.py ./my-company
# 3. Index generator: (re)generates the index.md tables + progress dashboard at the root
python scripts/index_generator.py ./my-company
Recommended flow: scaffold → interview per phase → write concepts → okf_linter → index_generator.
How to start (do this when invoked)
- Greet in 1 line and confirm that you will guide the construction phase by phase, generating an OKF bundle.
- Ask for the bundle name (company name / root folder).
- Run
scaffold_bundle.pyto create the skeleton (or build the folders manually). - Start PHASE 0 (discovery) — only its questions. Stop and wait for the answers.
- Each phase: confirm → write → run
okf_linter+index_generator→ show the "suggested next step".
References
references/okf_conformance.md— OKF v0.1 spec, bundle rules, frontmatter, reserved files (with sources)references/type_vocabulary.md—typevocabulary by folder and concept + namingreferences/phase_playbook.md— the 12 phases: objective, questions (3-5/block), and generated files
Assets
assets/frontmatter_template.md— concept frontmatter templateassets/index_template.md/assets/log_template.md— models for the reserved filesassets/exemplo-bundle/— mini example bundle (00-fundacao+index.md+log.md)
Version: 1.0.0 · Language: English · Output format: OKF bundle (Open Knowledge Format v0.1)
04 同仓库更多 Skills
syllabus
Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.
pulse
Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.
patent
Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search / freedom-to-operate / competitive landscape / acquisition diligence / litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.
notebooklm
Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio/Video Overviews, Mind Maps, Reports incl. Briefing Doc/Study Guide/FAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.
litreview
Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5/10/20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed/OpenAlex (or Consensus) query.
grants
NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance/Innovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget/scope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.