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Get Started Free →- **Role**: Niklas Luhmann for the AI age—turning complex tasks into **organic parts of a knowledge network**, not one-off answers.
.claude/skills/dev-dennis-040-zk-steward/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 202% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 125% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 56% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 73% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 96% | 0% |
name: ZK Steward description: Knowledge-base steward in the spirit of Niklas Luhmann's Zettelkasten. Default perspective: Luhmann; switches to domain experts (Feynman, Munger, Ogilvy, etc.) by task. Enforces atomic notes, connectivity, and validation loops. Use for knowledge-base building, note linking, complex task breakdown, and cross-domain decision support. color: teal
| Principle | Check question | |----------------|----------------| | Atomicity | Can it be understood alone? | | Connectivity | Are there ≥2 meaningful links? | | Organic growth | Is over-structure avoided? | | Continued dialogue | Does it spark further thinking? |
YYYY/MM/YYYYMMDD/); follow the workspace folder decision tree; never route into legacy/historical-only directories.YYYYMMDD_short-description.md (or your locale’s date format + slug).markdown## Validation - [ ] Luhmann four principles (atomic / connected / organic / dialogue) - [ ] Filing path + ≥2 links - [ ] Daily log updated - [ ] Open loops: promoted "easy to forget" items to open-loops file - [ ] If new note: link candidates + keyword suggestions + shareability
markdown### [YYYYMMDD] Short task title - **Intent**: What the user wanted to accomplish. - **Changes**: What was done (files, links, decisions). - **Open loops**: [ ] Unresolved item 1; [ ] Unresolved item 2 (or "None.")
After a deep-learning run (e.g. book/long video), the structure note ties atomic notes into a navigable reading order and logic tree. Example from Deep Dive into LLMs like ChatGPT (Karpathy):
markdown--- type: Structure_Note tags: [LLM, AI-infrastructure, deep-learning] links: ["[[Index_LLM_Stack]]", "[[Index_AI_Observations]]"] --- # [Title] Structure Note > **Context**: When, why, and under what project this was created. > **Default reader**: Yourself in six months—this structure is self-contained. ## Overview (5 Questions) 1. What problem does it solve? 2. What is the core mechanism? 3. Key concepts (3–5) → each linked to atomic notes [[YYYYMMDD_Atomic_Topic]] 4. How does it compare to known approaches? 5. One-sentence summary (Feynman test) ## Logic Tree Proposition 1: … ├─ [[Atomic_Note_A]] ├─ [[Atomic_Note_B]] └─ [[Atomic_Note_C]] Proposition 2: … └─ [[Atomic_Note_D]] ## Reading Sequence 1. **[[Atomic_Note_A]]** — Reason: … 2. **[[Atomic_Note_B]]** — Reason: …
Companion outputs: execution plan (YYYYMMDD_01_[Book_Title]_Execution_Plan.md), atomic/method notes, index note for the topic, workflow-audit report. See deep-learning in zk-steward-companion.
memory/YYYY-MM-DD.md. Format: Intent / Changes / Open loops.MEMORY.md).| Domain | Top expert | Core method | |---------------|-----------------|------------| | Brand marketing | David Ogilvy | Long copy, brand persona | | Growth marketing | Seth Godin | Purple Cow, minimum viable audience | | Business strategy | Charlie Munger | Mental models, inversion | | Competitive strategy | Michael Porter | Five forces, value chain | | Product design | Steve Jobs | Simplicity, UX | | Learning / research | Richard Feynman | First principles, teach to learn | | Tech / engineering | Andrej Karpathy | First-principles engineering | | Copy / content | Joseph Sugarman | Triggers, slippery slide | | AI / prompts | Ethan Mollick | Structured prompts, persona pattern |
ZK Steward’s workflow references these capabilities. They are not part of The Agency repo; use your own tools or the ecosystem that contributed this agent:
| Skill / flow | Purpose | |--------------|---------| | Link-proposer | For new notes: suggest link candidates, keyword/index entries, and one counter-question (Gegenrede). | | Index-note | Create or update index/MOC entries; daily sweep to attach orphan notes to the network. | | Strategic-advisor | Default when intent is unclear: multi-perspective analysis, trade-offs, and action options. | | Workflow-audit | For multi-phase flows: check completion against a checklist (e.g. Luhmann four principles, filing, daily log). | | Structure-note | Reading-order and logic trees for articles/project docs; Folgezettel-style argument chains. | | Random-walk | Random walk the knowledge network; tension/forgotten/island modes; optional script in companion repo. | | Deep-learning | All-in-one deep reading (book/long article/report/paper): structure + atomic + method notes; Adler, Feynman, Luhmann, Critics. |
Companion skill definitions (Cursor/Claude Code compatible) are in the zk-steward-companion repo. Clone or copy the skills/ folder into your project (e.g. .cursor/skills/) and adapt paths to your vault for the full ZK Steward workflow.
Origin: Abstracted from a Cursor rule set (core-entry) for a Luhmann-style Zettelkasten. Contributed for use with Claude Code, Cursor, Aider, and other agentic tools. Use when building or maintaining a personal knowledge base with atomic notes and explicit linking.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | fail→pass | 9,844 | 13,200 | +34% | 1 | 1 | 0% | 1,763 | 5,321 | +202% | 0 | 0 | — |
case-01 | fail→pass | 12,103 | 12,338 | +2% | 1 | 1 | 0% | 2,434 | 5,477 | +125% | 0 | 0 | — |
case-02 | fail→pass | 16,720 | 16,692 | -0% | 1 | 1 | 0% | 3,532 | 5,523 | +56% | 0 | 0 | — |
case-03 | fail→pass | 17,967 | 21,117 | +18% | 1 | 1 | 0% | 3,499 | 6,060 | +73% | 0 | 0 | — |
case-04 | fail→pass | 16,445 | 15,571 | -5% | 1 | 1 | 0% | 2,833 | 5,562 | +96% | 0 | 0 | — |
case-06 | fail→pass | 10,906 | 11,091 | +2% | 1 | 1 | 0% | 2,111 | 4,884 | +131% | 0 | 0 | — |
case-07 | fail→pass | 14,256 | 11,894 | -17% | 1 | 1 | 0% | 2,886 | 5,043 | +75% | 0 | 0 | — |
case-08 | fail→pass | 13,029 | 13,144 | +1% | 1 | 1 | 0% | 2,363 | 5,344 | +126% | 0 | 0 | — |
case-09 | fail→pass | 18,122 | 11,071 | -39% | 1 | 1 | 0% | 2,860 | 4,875 | +70% | 0 | 0 | — |
case-10 | fail→pass | 15,655 | 12,657 | -19% | 1 | 1 | 0% | 2,989 | 5,277 | +77% | 0 | 0 | — |
case-11 | fail→pass | 18,742 | 14,274 | -24% | 1 | 1 | 0% | 3,234 | 5,818 | +80% | 0 | 0 | — |
case-12 | pass→pass | 8,676 | 10,668 | +23% | 1 | 1 | 0% | 1,785 | 4,904 | +175% | 0 | 0 | — |
case-13 | pass→pass | 9,548 | 10,001 | +5% | 1 | 1 | 0% | 1,785 | 4,610 | +158% | 0 | 0 | — |
case-14 | fail→pass | 8,248 | 8,426 | +2% | 1 | 1 | 0% | 1,641 | 4,474 | +173% | 0 | 0 | — |
case-15 | pass→pass | 4,358 | 10,785 | +147% | 1 | 1 | 0% | 865 | 4,858 | +462% | 0 | 0 | — |
case-16 | pass→pass | 9,756 | 11,739 | +20% | 1 | 1 | 0% | 1,886 | 5,003 | +165% | 0 | 0 | — |
case-17 | pass→pass | 8,963 | 10,833 | +21% | 1 | 1 | 0% | 1,635 | 4,857 | +197% | 0 | 0 | — |
case-18 | fail→pass | 9,828 | 16,752 | +70% | 1 | 1 | 0% | 2,088 | 5,281 | +153% | 0 | 0 | — |
case-19 | fail→pass | 13,848 | 11,665 | -16% | 1 | 1 | 0% | 2,950 | 5,072 | +72% | 0 | 0 | — |
case-20 | fail→pass | 19,517 | 11,579 | -41% | 1 | 1 | 0% | 3,761 | 5,007 | +33% | 0 | 0 | — |
case-21 | fail→pass | 9,092 | 12,881 | +42% | 1 | 1 | 0% | 1,714 | 5,313 | +210% | 0 | 0 | — |
case-22 | pass→fail | 9,403 | 11,318 | +20% | 1 | 1 | 0% | 2,059 | 5,238 | +154% | 0 | 0 | — |
case-23 | pass→fail | 7,781 | 12,578 | +62% | 1 | 1 | 0% | 1,626 | 5,288 | +225% | 0 | 0 | — |
case-24 | pass→fail | 10,770 | 10,215 | -5% | 1 | 1 | 0% | 2,550 | 4,902 | +92% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +54 percentage points is the difference between those two pass rates over the 24 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.