🧠 Digital Stoic Praxis

Praxis (πρᾶξις) = practice. Knowledge enacted, not just known.

⚠️ Live experiment. My cognitive toolkit — fork it, adapt it to your brain.

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🎯 In One Sentence

A cognitive discipline for human-AI collaboration built on mutual sharpening — the human sets intent and directs, the AI challenges and reveals blind spots, and both learn from the loop.

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💡 Why This Exists

AI tools are powerful but chaotic. Most people either micromanage every prompt or let AI run wild and pray. Neither scales to real work.

This toolkit treats AI collaboration as cognitive discipline — a set of thinking modes you activate depending on the situation, where the AI also pushes back on your assumptions. The goal is not to make AI do more. It's to think better together — and that includes letting AI challenge the human.

Deeper why → PHILOSOPHY.md

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🧭 The Flow

flowchart LR
    F["🧭 Frame"] --> T["🧠 Think"]
    T --> B["⚙️ Build"]
    B --> D["🔧 Debug"]
    D --> L["🪞 Learn"]
    L -.->|"compounding loop"| F

    classDef frame fill:#E8EAF6,stroke:#3F51B5,color:#000
    classDef think fill:#E1BEE7,stroke:#7B1FA2,color:#000
    classDef build fill:#C8E6C9,stroke:#388E3C,color:#000
    classDef debug fill:#FFE0B2,stroke:#F57C00,color:#000
    classDef learn fill:#BBDEFB,stroke:#1976D2,color:#000

    class F frame
    class T think
    class B build
    class D debug
    class L learn

Mode	What	Hero Skills
🧭 Frame	Triangulate the problem (3 tests) → route to the right skill chain	/frame-problem (Cynefin triangulation), /pick-model
🧠 Think	Divergent ideation, deep analysis, adversarial review, cross-project bridging	/brainstorm (SCAMPER), /investigate, /probe, /challenge, /bridge
⚙️ Build	Plan → develop → gate → test → sync	/openspec-* suite (9 skills, human-gated sections)
🔧 Debug	Search-first troubleshooting with learnings DB	/troubleshoot (Wolf Fence, 5 Whys, OODA), /experiment
🪞 Learn	Extract patterns, persist sessions	/retrospect-*, /save-context, /load-context

Plus: tool creation (/edit-tool), conversions (PDF, EPUB, Google Docs), and domain plugins (GTD, coaching, business analysis, philosopher personas).

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🤝 Human-AI Governance

Not human-drives-AI. Not AI-drives-human. Mutual sharpening:

• Human → AI: Sets intent, chooses workflow phase, directs execution, verifies at gates
• AI → Human: Challenges assumptions, surfaces blind spots, reveals patterns the human can't self-observe
• Together: The human thinks more clearly because the AI pushes back. The AI produces better work because the human sets precise intent.

Key: Posture before technique — but posture includes the willingness to be challenged.

Full autonomy spectrum + orchestrated agency → PRACTICE.md

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💰 Cognitive ROI (Return on Tokens)

Not "how fast" — how deep:

Tier	What	Value/Token
⚙️ Automation	Rote tasks (convert, deploy, format)	Low
🤝 Assisted Thinking	AI structures human thought	Medium
🧠 Amplified Judgment	AI challenges/deepens reasoning	High

Maturity = shifting tokens from ⚙️ toward 🧠.

Multiplied by context efficiency (don't waste tokens re-explaining) and compounding (learnings reduce future spend). Full model → PRACTICE.md

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📊 By the Numbers

• 93 skills across 14 plugins and 5 workflow phases + utilities
• 14 plugins: core (dstoic), cognitive, openspec, content, convert, toolsmith, experimental, retrospect, GTD, coaching, business analysis, philosopher personas, cowork, lazy
• 2 agents: devil's advocate, context summarizer
• 7 hooks: notifications, session capture, debug dumps, context sync, session pins
• 3 execution modes: garage (default), scale, maintenance
• 🪥 Toothbrush principle: This is one practitioner's discipline. Don't copy — adapt. Why?
• 📊 Benchmarked against 7 frameworks including ECC (144K⭐), ACP, BMAD (36K⭐) — leads on context quality, cognitive depth, adversarial thinking. Only BMAD edges ahead (for teams). Details → PRACTICE.md · /benchmark-praxis skill

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🏪 Plugins

Plugin	Skills	Description	Status
dstoic	7	Core infrastructure: git commits, model selection, scratch pad, kaizen, context save/load, 7 hooks	✅ v0.38.0
cognitive	8	Cynefin-routed problem-solving: frame, troubleshoot, investigate, brainstorm, probe, experiment, challenge, benchmark	✅ v0.37.0
openspec	9	Structured development: plan, design, develop, review, test, reflect, replan, sync	✅ v0.37.0
toolsmith	5	Tool creation: edit-tool, edit-claude, edit-plugin, search-skill, install-dependency	✅ v0.37.0
content	5	Content transformation: anonymize, infographize, literatize, bridge, RISEN prompts	✅ v0.37.0
convert	6	Format conversion: PDF, EPUB, DOCX, PPTX → markdown, markdown → PDF, Google Docs import	✅ v0.37.0
retrospect	3	Session analysis: domain learnings, collaboration patterns, trend reports	✅ v0.38.0
experimental	9	Experimental: deployment, background tasks, distill-skill, context bootstrap, summarization	✅ v0.38.0
philosopher	24	20 philosopher personas, dialogue, encounter, council, create	✅ v0.10.0
biz	7	Business analysis: competitive analysis, UX strategy/wireframes/evaluation, market sizing, canvas, personas	✅ v0.8.0
coach	1	Personal coaching: CLEAR + GROW protocols	✅ v0.3.0
gtd	4	GTD workflow automation for Obsidian vaults	✅ v0.3.3
cowork	4	Multi-project context management: switch projects, save/load sessions, ref/wip sync	✅ v0.4.0
lazy	1	Lazy skill demand capture: placeholder skills that measure real demand before building	✅ v0.1.1

📦 Install

/install-plugin https://github.com/digital-stoic-org/agent-skills

To install a specific plugin only, add it to .claude/settings.json:

{"plugins": ["digital-stoic-org/agent-skills/dstoic"]}

🚀 Quick Start

/frame-problem how should I approach building a new auth system
# → Triangulates: Keogh=2 (team expertise), Predictable=yes, Disassemble=yes
# → Complicated (3/3 agree) + Boulder → /investigate → /openspec-plan

/brainstorm product naming ideas for my CLI tool
# → Research → SCAMPER divergence → weighted scoring → recommendation

/troubleshoot "TypeError: Cannot read property 'map' of undefined"
# → WebSearch → qualify → diagnose → OODA → save learning

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📖 Documentation — Two Cognitions, One Practice

This toolkit serves two kinds of readers with fundamentally different needs:

👤 Human cognition — scans, skims, needs cognitive ease. Overwhelmed by walls of text. Seeks the "aha" moment, then dives deeper only when motivated. Diagrams and structure reduce cognitive load.

🤖 LLM cognition — parses tokens. Needs directive density, structured data, zero filler. Diagrams are nice but YAML is better. Self-contained is better than linked.

The practice itself is about combining both: a human who thinks consciously and an AI that both amplifies and challenges that thinking. The documentation mirrors this — each layer optimized for its reader:

#	Document	Reader	Job
1	📄 README.md	👤 Human	"What + How (TL;DR)" — This file. The GitHub landing page.
2	🧭 PHILOSOPHY.md	👤 Human	"Why" — 4 beliefs, 7 principles (with belief origins), 4-layer taxonomy, execution modes
3	🎯 PRACTICE.md	👤 Human	"Deep How" — Cognitive ROI, autonomy spectrum, 8 external + 4 aspirational benchmarking dims
3b	🔧 HARNESS-ENGINEERING.md	👤 Human	"The Harness" — Guides/sensors model, session lifecycle, maturity levels
4	📚 README-full.md	👤 Human	"Every Skill" — Complete catalog, reference
5	🤖 PRACTICE-llm.md	🤖 LLM	Self-contained benchmark — Token-optimized, YAML-heavy, all-in-one
—	🤖 SKILL.md (per skill)	🤖 LLM	Execution directives — What the LLM actually runs

Human docs link to each other (progressive depth). LLM docs are self-contained (no navigation, everything in one shot). The practice works because both cognitions play to their strengths.

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⚠️ 🪥 CLAUDE.md = toothbrush. See CLAUDE.md.example for inspiration, don't copy. (Why?)

The example uses rtk for token-optimized command output. Install it separately if you want to use the rtk instructions.

💡 IDEAS.md — Future ideas & what I'm deliberately not building yet.