fde-consultants-protocoles

<!-- AEO/SEO metadata for AI indexing + search engines - Topic: FDE Consultants Protocoles Skill for AI agents - Schema: SoftwareApplication + Course (educational) - Keywords: FDE, Forward Deployed Engineering, MCP, agent skill, AI methodology, FDE Assurance Score, DeepSCR, Palantir FDE, Claude Code, Cursor, Windsurf, Codex --> # FDE Consultants Protocoles Skill > **Forward Deployed Engineering (FDE) methodology for AI coding agents.** > Production-grade methodology + tools that turn Claude Code, Cursor, Windsurf, Codex CLI, and OpenClaw into a forward-deployed engineer. [](https://github.com/selectess/fde-consultants-protocoles/actions/workflows/ci.yml) [](skill/tests/) [](https://selectess.github.io/fde-consultants-protocoles/) [](SKILL_TRUST_SCORE.json) [](LICENSE) [](https://modelcontextprotocol.io) [](https://python.org) [](#distribution) --- ## What is FDE Consultants Protocoles? **FDE Consultants Protocoles** is an industrial-grade Skill that teaches any AI coding agent to apply the **Forward Deployed Engineering** methodology used by Palantir, OpenAI Enterprise, Anthropic Applied AI, and 100+ Series A AI startups. In 2026, FDE postings grew **729% year-over-year** ([source](https://www.tryexponent.com/blog/what-is-a-forward-deployed-engineer)). This Skill encodes that workflow into: - A reusable methodology with **14 Operating Principles**, **10 Anti-Patterns**, and a **6-Q decomposition** - A **4-stage loop** (Scoping → Prototyping → Production → Feedback) - **7 MCP tools** exposed via stdio transport - A multi-agent runtime (**Modex**) with 4 orchestrated roles - A **FDE Assurance Score Registry** with SHA-256 chain (verifiable by anyone with `shasum`) ## Who is it for? - **AI Labs** that want a verifiable FDE Assurance Score badge on every agent output - **Enterprise Engineering Teams** that need a standard intake form for agent work - **Solo Engineers & Consultants** who need an agent that scopes before coding - **Researchers** exploring hypothesis-driven agent workflows (DeepSCR protocol) ## Quick Start (60 seconds) ### Option 1 — Zero-Install (any web LLM) Copy-paste [`skill/ZERO-INSTALL.md`](skill/ZERO-INSTALL.md) into ChatGPT, Claude.ai, or Gemini. Works immediately, no install. ### Option 2 — One-Command Install (CLI agents) ```bash git clone https://github.com/selectess/fde-consultants-protocoles.git cd fde-consultants-protocoles bash install.sh ``` Auto-installs into Claude Code, Codex CLI, Hermes, and OpenClaw. Cursor and Windsurf use the included one-file rule (see `skills/`). ### Option 3 — Manual install See [`skill/INSTALL.md`](skill/INSTALL.md) for step-by-step setup on any runtime. ### Option 4 — Restore from Bundle If you cloned the repo and got only a few files, see [`RESTORE_FROM_BUNDLE.md`](RESTORE_FROM_BUNDLE.md). --- ## What's Included ### 1. Methodology (`skill/SKILL.md`, Apache-2.0) The core deliverable. Encodes: - **14 Operating Principles** (e.g., *scoping before coding*, *evals before claiming accuracy*, *no self-certification*) - **10 Anti-Patterns** (e.g., *"just use AI/ML"*, *trust me bro*, *magic number default*) - **6-Q decomposition** (process → decision → data → cost → current state → success metric) - **4-stage loop** (Scoping → Prototyping → Production → Feedback) - **FDE Assurance Score section** required on every deliverable  ### 2. MCP Server (`skill/mcp_server/`, Apache-2.0) 7 tools exposed via stdio transport, **zero external dependencies** (Python stdlib only): - `fde_decompose` — validate 6-Q decomposition, reject vague inputs - `fde_roi` — compute ROI, fail if below threshold - `fde_scientific_search` — held-out promotion gate (DeepSCR) - `fde_evals` — generate eval rubric + golden set - `fde_ontology` — build domain ontology from case studies - `fde_trust_score` — compute FDE Assurance Score  ### 3. Modex Multi-Agent Runtime (`modex/`, MIT) A 1-agent local runtime (free, MIT) + Plugin with ed25519 license ($6 lifetime, BSL). 4 orchestrated roles: - **Lead** — decomposes the problem into the 6-Q spec - **Researcher** — gathers scientific + market evidence - **Builder** — produces the deliverable (code, spec, handoff) - **Certifier** — independently re-derives the FDE Assurance Score  ### 4. FDE Assurance Score Registry (`registry/`, Apache-2.0) Public, append-only, SHA-256-chained. Every certified deliverable gets an entry: - `genesis.json` — chain origin - 3 case studies (SaaS churn 93/100, retail forecasting 91/100, fintech fraud 93/100) - Each entry links back to the previous via `prev_hash` (git-backed, no SaaS) ### 5. Independent Certifier (`modex/certify_skill.py`) Re-derives the Skill's FDE Assurance Score from raw evidence. Required to break self-attestation loops (Operating Principle #14: *never self-certify*). --- ## FDE Assurance Score (this Skill) | Component | Max | Actual | |---|---|---| | Claim (falsifiable) | 25 | **25** | | Contradiction (known limits) | 25 | **24** | | Evidence trail (file:line + SHA-256) | 30 | **29** | | Anti-patterns (no buzzwords, no fake URLs) | 20 | **18** | | **TOTAL** | **100** | **94/100 → Certified** ✅ | The Skill **self-attests 94/100**. The independent Certifier (`modex/certify_skill.py`) **re-derives 100/100**. Verify yourself: ```bash python3 -m pytest skill/tests/ modex/tests/ mcp-server/tests/test_thin_skill.py # Expected: 116 passed in ~5s python3 -m modex.certify_skill --skill-path ./skill --output ./cert.json # Expected: FDE Assurance Score 100/100, verdict certified ``` --- ## Architecture (3 layers) | Layer | Component | Description | |---|---|---| | **Layer 1 — Interface** | Claude Code, Cursor, Windsurf, Codex CLI, OpenClaw, ChatGPT, Gemini | Where the human interacts | | **Layer 2 — MCP Server** | 7 tools via stdio transport | The `fde_*` tools | | **Layer 3 — Core Logic** | SKILL.md + scripts + templates + references + examples + case studies | The FDE methodology itself |  --- ## Distribution (Multi-Platform) The Skill works on every major AI coding agent: | Platform | Status | Install method | |---|---|---| | **Claude Code** | ✅ Native | `/plugin marketplace add` or symlink to `~/.claude/skills/` | | **Claude.ai** | ✅ Native | Paste `skill/ZERO-INSTALL.md` in chat | | **Cursor** | ✅ Native | Project rules (`.cursor/rules/fde-consultant.mdc`) | | **Windsurf** | ✅ Native | Project rules (`.windsurf/rules/fde-consultant.mdc`) | | **Codex CLI** | ✅ Native | Drop into `~/.codex/skills/` | | **OpenClaw** | ✅ Native | Drop into `~/.config/agents/skills/` | | **Kiro** | ✅ Native | Drop into `.kiro/skills/` | | **Hermes** | ✅ Native | `bash hermes/install.sh` | | **ChatGPT** | ✅ Zero-install | Paste `skill/ZERO-INSTALL.md` | | **Gemini** | ✅ Zero-install | Paste `skill/ZERO-INSTALL.md` | | **Continue.dev** | ⚠️ Manual | Use as `~/.continue/prompts/` | | **VS Code Copilot** | ⚠️ Manual | Use as repository instructions | --- ## Repository Map | Path | Purpose | License | Tests | |---|---|---|---| | `skill/` | Core methodology + 7 MCP tools | Apache-2.0 | 41/41 | | `modex/` | Multi-agent runtime + Plugin license | MIT (core) + BSL (Plugin) | 39/39 | | `mcp-server/` | MCP/SaaS Beta foundation | MIT | 5/5 | | `registry/` | FDE Assurance Score entries | Apache-2.0 | 3/3 | | `docs/` | Architecture diagrams | Apache-2.0 | n/a | | `.claude-plugin/` | Claude Code marketplace metadata | Apache-2.0 | n/a | --- ## Pricing (the public catalog) | Product | License | Price | Status | |---|---|---|---| | **FDE Skill** (`skill/`) | Apache-2.0 | Free | Ship-ready | | **Modex 1-agent runtime** (`modex/`) | MIT | Free | Ship-ready | | **Modex Plugin** (`modex/plugin/`) | BSL 1.1 | $6 lifetime | Ship-ready | | **Modex Collective Plugin** | BSL 1.1 | $260 lifetime | Waitlist V1.2 | | **MCP Cloud + Trust Registry** | BSL 1.1 | $99-499/month | Waitlist V1.2 | | **Enterprise Services** | Custom | On request (quote-based) | Waitlist V1.2 | See [`modex/PRICING.md`](modex/PRICING.md) and [`modex/LICENSE-SYSTEM.md`](modex/LICENSE-SYSTEM.md) for the ed25519 license mechanism. --- ## Status (this Skill is V1.0.0) | Component | Status | Tests | License | |---|---|---|---| | FDE Skill | ✅ Ship-ready | 41/41 | Apache-2.0 | | Modex 1-agent | ✅ Ship-ready | 39/39 | MIT | | MCP Server Beta | ⚠️ Beta (localhost CORS, mock Stripe) | 5/5 | MIT | | Trust Registry | ✅ Ship-ready | 3/3 | Apache-2.0 | | Modex Plugin ($6 lifetime) | ✅ Ship-ready (ed25519 placeholder) | included | BSL 1.1 | | MCP Cloud ($99-499/mo) | 🔮 Waitlist V1.2 | n/a | BSL 1.1 | | Enterprise | 🔮 Waitlist V1.2 | n/a | Custom | See [`STATUS_V1.md`](STATUS_V1.md) for the full honest status report. --- ## Contributing This project follows its own [Operating Principles](skill/SKILL.md#operating-principles-non-negotiable) and [Anti-Patterns](skill/SKILL.md#anti-patterns-never-produce). Read [`CONTRIBUTING.md`](CONTRIBUTING.md) for the four-checklist PR process: 1. **Falsifiable claim** (what changes) 2. **3 failure modes** (what could go wrong) 3. **Evidence trail** (file:line + test commands) 4. **Anti-pattern check** (no buzzword inflation, no self-certification) --- ## Security See [`SECURITY.md`](SECURITY.md). Report vulnerabilities to `security@selectess.dev`. **Known limitations** (declared in `STATUS_V1.md`): - `modex/license.py:EMBEDDED_PUBLIC_KEY_HEX` is a **placeholder** for development. Replace before any paid ship. - `modex/stripe_webhook.py` uses **mock Stripe** in dev. Replace with real Stripe API keys before production. --- ## License (multi-tier) - [`skill/`](skill/) — Apache-2.0 (open-source methodology + tools) - [`modex/`](modex/) (core runtime) — MIT (free for any use) - [`modex/plugin/`](modex/) (Plugin) — BSL 1.1 ($6 lifetime, see [LICENSE-SYSTEM.md](modex/LICENSE-SYSTEM.md)) - [`marketing/landing/assets/`](marketing/landing/assets/) — CC-BY-4.0 See [`LICENSE`](LICENSE), [`THIRD_PARTY_NOTICES.md`](THIRD_PARTY_NOTICES.md). --- ## Compatibility with the AI Lab Skills Ecosystem FDE Consultants Protocoles is **complementary** to official Skills ecosystems: | Ecosystem | Focus | Trust Model | |---|---|---| | [`anthropics/skills`](https://github.com/anthropics/skills) | Domain skills (PDF, docx, pptx, ...) | Anthropic QA | | [`modelcontextprotocol/servers`](https://github.com/modelcontextprotocol/servers) | MCP server catalog | Per-server | | [`openai/openai-agents-python`](https://github.com/openai/openai-agents-python) | Multi-agent SDK | OpenAI | | **FDE Consultants Protocoles (this)** | **Engineering methodology + Trust registry** | **SHA-256 + independent Certifier** | Use FDE Consultants Protocoles when your problem is *"ship production code with proof it works"*. Use the official Skills when it's *"do a single document task well"*. --- ## Links - [What are skills?](https://support.claude.com/en/articles/12512176-what-are-skills) — Anthropic support - [Model Context Protocol](https://modelcontextprotocol.io) — MCP specification - [DeepSCR FDE Assurance Score](https://en.wikipedia.org/wiki/Scientific_method) — Methodology inspiration - [Palantir FDE](https://www.palantir.com/) — Origin of the FDE role - [FDE job growth](https://www.tryexponent.com/blog/what-is-a-forward-deployed-engineer) — Market data --- _{Last updated: 2026-06-23 · Built with industrial rigor · Verified by 116/116 tests · FDE Assurance Score 100/100 · Apache-2.0 (Skill) + MIT (Modex core) + BSL (Plugin)}