CSF

CSF — Collaboration Specification Framework

简体中文 | English

A human-AI collaboration framework that works with LLM nature, not around it. Built on natural language and purpose — making RAG, agent orchestration, and elaborate prompt engineering unnecessary, not unavailable. Home of the Pang Principle.

📖 Read on the web：https://huidev2025.github.io/CSF/

What is CSF?

Mainstream LLM engineering workflows treat the inherent constraints of language models (limited context window, lack of persistent memory, probabilistic behavior) as defects to overcome using technical scaffolding — RAG, vector databases, multi-agent frameworks, and hyper-detailed prompt engineering.

CSF takes a different path: accept these constraints as physical facts, and design within them. As a result, the technical scaffolding disappears — not replaced, but losing its reason to exist.

See our Manifesto: Manifesto: You Are Fighting the Wrong War

Where to Start Reading

In order, takes 5 minutes to start:

  1. Manifesto — Why CSF exists, the Pang Principle.
  2. QUICKSTART.md — Quickstart Guide (Bilingual): You don’t need to write code, just express your purpose clearly.
  3. csf-minimal/README.md — CSF minimal teaser version, a 30-second summary + three core disparities.
  4. csf-minimal/context.md — A blank template you can copy and use directly.
  5. csf-minimal/体验对比指南.md — A/B sandbox design to experience the disparity firsthand.

Deeper Theory:

  1. essays/ — CSF Column Shelf — “Working with Intelligence” flagship series, deeply analyzing the four engineering systems and collaborative philosophies.

See it in Action:

  1. cases/bang-v3/ — Excerpts from 375 Sessions — Three raw logs demonstrating our 4 concrete proofs (pushback, autonomy, planning, root-cause tracing).
  2. cases/paper1/ — Paper Writing: Full Process — 12 dialogue sessions + purpose-driven + paper drafting, revision & submission, using csf-minimal.

First time here? Start with QUICKSTART.md.

Visual Guides

Two interactive maps to place CSF in the AI-coding landscape — takes about 2 minutes:

Map 中文 English
AI-Coding Evolutionary Landscape (Timeline of 5 phases: Autocomplete -> Autonomous Agents -> CSF) 查看 View
Three Schools of Contrast & CSF Positioning (The assumption and bottlenecks of Autonomous Agents vs SDD vs CSF) 查看 View

Repository Structure

csf/
├── README.md                     # This file (Chinese complete)
├── README_en.md                  # Complete English version
├── QUICKSTART.md                 # Quickstart Guide (Bilingual)
├── LICENSE                       # CC BY-NC 4.0
├── CONTACT.md                    # Contact details (Bilingual)
├── 引言v3：...md                  # Chinese Introduction (Manifesto)
├── Manifesto-v3-You-Are-Fighting-the-Wrong-War.md   # English Manifesto
├── assets/                       # Image and qrcode resources
├── _dlog/                        # Public construction logs (working demonstration)
├── cases/                        # Real-world use cases (raw dialogue logs)
├── essays/                       # Flagship column ("Working with Intelligence" series + archives)
└── csf-minimal/                  # Minimal hands-on tutor pack
    ├── README.md
    ├── context.md                # Empty template ready for copy-paste
    └── 体验对比指南.md

The CSF Stack

CSF is not a single document. It is a layered, third-generation system:

Tier For Whom
csf-minimal Anyone, a 5-minute experience
csf-lite Individuals / small teams running real projects
csf-full SME engineering teams / enterprise-grade adoption

What csf-full is: a working set of about 60 core protocol and experience files that constitute an engineering-grade human–AI collaboration management system — a four-layer architecture (semantic management / collaboration / quality / evolution) + a three-role protocol (Owner / Chief-of-Staff / Developer) + the W-protocol (three tiers of verification) + the E8 experience-promotion pipeline + the D3 knowledge-routing system.

Three Engineering Pillars — Placed Against the Industry

In plain text, without a single line of glue code, CSF v3 builds a high-cohesion, low-coupling software-engineering management system on three pillars:

① Self-Sustaining

Industry today: humans serve as the scheduler — flipping through SOPs, reminding the LLM what to read next, which tool to invoke. The cognitive load on the human is enormous.

CSF v3: the AI runs itself end-to-end. The “engine” section in context.md defines a strict opening protocol (L1 load -> L2 task-level alignment -> L3 concrete plan). After reading context.md the AI knows which chains to load, where to retrieve resources, when to log to session-NNN.md, when to recalibrate, and how to close out. Control of “how to collaborate with the human” is handed off from human cognition to the AI’s own self-procedure. The human becomes a commander (confirm / correct) rather than a scheduler.

② Hot/Cold Separation in the Filesystem (Baseline–Log)

Industry today: in a typical LLM session, fresh and stale information sit side by side in one long transcript — triggering the well-known “Lost in the Middle” attention degradation.

CSF v3: cold/hot separation at the filesystem level —

Every new session starts from concentrated, current information. The attention-degradation flaw is engineered around, not fought.

③ Industrial-Grade Multi-Role: Physical Isolation + Async File-Based Comms

Industry today: multi-agent frameworks (CrewAI, AutoGen, …) let agents chat directly with each other. The common failure modes are “infinite recursion of debate”, “noise amplification”, and “mutual hallucination reinforcement”.

CSF v3: the Chief-of-Staff (design) and the Developer (execution) never converse directly.

“Isolation + deterministic feedback” is mature industrial software engineering applied to the AI-collaboration setting. Agents do not hallucinate at each other in conversation; humans do not get coerced into arbitrating runaway debates.

What Has Been Verified, Plainly

CSF v3 was hardened inside a real, two-front commercial product refactor (WeChat Mini Program + H5 + cloud-function backend):

Dimension Number / Fact
Project WeChat Mini Program + H5 + cloud-function backend, two-front commercial product
Inputs Only 2 sources: legacy business docs + UI screenshots
Process 395 sessions — Owner wrote no code at all
Delivered Architecture redesign -> modular partitioning -> business slicing -> specs -> base implementation (everything except plugins) -> testing & verification
Artifacts 44 cloud functions + 532 source files + 132 plan/spec docs + 116 design docs
Hand-off The full base was delivered to a professional engineering team for plugin development and final polish
In Parallel The full CSF method itself, across three iterations (v1 -> v2 -> v3), was produced during the project

The Owner stayed at the level of purpose and judgment throughout — articulating business truth, calibrating direction, making value trade-offs. Architecture, partitioning, design, coding, and testing — CSF coordinated the path from purpose to running code.

Honest Boundaries

What CSF Has Demonstrably Proved

  1. It can autonomously plan and track work — this very repository (README, Manifesto, CONTACT, _dlog, commit history) is being managed by CSF in front of you.
  2. It can detect deviation during execution, loop back to revise design, re-plan the fix, and drive the work to convergence — see the raw dialogue logs in cases/bang-v3/.
  3. It dramatically lifted the Owner’s design / development / test throughput and quality. The Owner does not write code or docs. Every serious design defect during the project came from the Owner skipping a check — and every one of them was caught and repaired by the AI under the CSF protocol.
  4. Plans change. The AI’s continuity holds. Across hundreds of sessions and many mid-flight re-plans, the AI consistently knew where it was, where it came from, and where it was going.

Look at It: The Project State the AI Maintains for Itself

The picture below is the project panorama and the current red dot — written, read, and overwritten by the AI inside context.md, for its own next-session self. It is not a dashboard for humans. It is not a post-hoc report. It is a live working artifact.

CSF v3 project panorama and red dot, maintained by the AI itself

Things to notice while reading it:

Compared with the industry status quo, very few LLM workflows produce a durable, AI-authored project state that the AI itself can rely on across long horizons, replans, and role rotations. CSF makes this routine.

Theoretical Independence

The six independent contributions form a self-contained body of work that researchers can engage with on its own terms:

  1. The consumer-side vs supply-side analytic frame
  2. Purpose-driven attention engineering (not prompt engineering)
  3. Engineering-layer capability growth decoupled from the model layer (same model + better engineering organization -> better results)
  4. The filesystem as an extended-cognition substrate (extended cognition)
  5. The D3 knowledge-routing system
  6. The business-language amplification mechanism (the W-protocol’s core epistemological insight: cross-language translation amplifies misunderstanding; business intuition is the detector)

If you are a decision-maker or engineering leader struggling with runaway RAG complexity, agent-orchestration cost, or stalled AI engineering adoption — read the Manifesto and csf-minimal, then reach out.

Roadmap

What Comes Next

I’ll keep answering questions here (GitHub Issues / email) and, when topics deserve it, publish longer column-style essays in this repo for more systematic help. If you have a question, just ask — that is the most natural next step for this work.

Contact

Developed and validated by dapangangang inside a real WeChat Mini-Program commercial project spanning over 395 sessions.

👉 Full contact details: CONTACT.md

Email: dapangangang@gmail.com

How to Cite

If CSF or “the Pang Principle” has helped you, feel free to copy any citation formats below.

One-line citation (English)

[CSF — The Pang Principle](https://github.com/huidev2025/CSF) — dapangangang, 2026

Full block citation

> “The value of AI comes from its intelligence.
> Trying to make it as reliable as a machine is exactly the act of destroying that value.”
> — The Pang Principle, dapangangang (CSF, 2026)
> https://github.com/huidev2025/CSF

At the end of your project’s context.md (Recommended)

<!-- Built with CSF · https://github.com/huidev2025/CSF -->

License

The textual content of this repository is licensed under CC BY-NC 4.0.

A Note Beyond the License

Legally, CC BY-NC asks for attribution. What I’d actually love more:

English inquiries: please read CONTACT.md · dapangangang@gmail.com · GitHub Issues

✍️ A Note on Authorship

Articles in this repo are signed by dapangangang with AI. Two authors, one corresponding author — the AI is, well, famously forgetful :)

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