Skill Graphs: Building Deep Knowledge Systems for AI Agents

people underestimate the power of structured knowledge. it enables entirely new kinds of applications

right now people write skills that capture one aspect of something. a skill for summarizing, a skill for code review and so on. (often) one file with one capability

thats fine for simple tasks but real depth requires something else

imagine a therapy skill that provides relevant information about cognitive behavioral patterns, attachment theory, active listening techniques, emotional regulation frameworks and so on

a single skill file cant hold that

skill graphs

a skill graph is a network of skill files connected with wikilinks

instead of one big file you have many small composable pieces that reference each other. each file is one complete thought, technique or skill and [[wikilinks between them create a traversable graph]]

a skill graph applies the same skill discovery pattern recursively inside the graph itself

every node has a yaml description the agent can scan without reading the whole file

every wiki link carries meaning because its woven into prose so the agent follows relevant paths and skips what doesnt matter

progressive disclosure:

index → descriptions → links → sections → full content

most decisions happen before reading a single full file

the primitives

you already have everything you need

• wikilinks that read as prose in sentences, so they carry meaning not just references
• yaml frontmatter with descriptions so the agent can scan without reading full files
• MOCs (maps of content) that organize clusters of related skills into navigable sub-topics

skill links to other skills which link to other skills and the graph goes as deep as the domain requires

arscontexta plugin

arscontexta is a skill graph that teaches your agent how to build skill graphs

(okay actually its about building knowledge bases but thats the same thing...)

~250 connected markdown files that teach an agent how to build a massive knowledge base aka skill graph for you

one skill file couldnt do that

but things change if you build a graph of interconnected research claims (/skills) about cognitive science, zettelkasten, graph theory, agent architecture where each piece links to others, each one composable and the whole thing is traversable

what this enables

think about it:

• a trading skill graph: risk management, market psychology, position sizing, technical analysis, each piece linked to related concepts so context flows between them
• a legal skill graph: contract patterns, compliance requirements, jurisdiction specifics, precedent chains, all traversable from one entry point
• a company skill graph: org structure, product knowledge, processes, onboarding context, culture, competitive landscape

none of these fit in one file but all of them work as graphs

how to build one

the easy way: install the arscontexta claude code plugin, pick the research preset and point it at any topic

it sets up the markdown folder structure for you and then you fill it with /learn and /reduce

the manual way its simpler than you think

a skill graph doesnt need to live in your .claude/skills/ folder. the key is an index file that tells the agent what exists and how to traverse it

heres what an index looks like for a knowledge work skill graph:

1# knowledge-work
2
3Agents need tools for thought too. Just as Zettelkasten, evergreen notes, and memory palaces gave humans external structures to think with, agent-operated knowledge systems give agents external structures to think with.
4
5## Synthesis
6
7Developed arguments about how the pieces fit together:
8
9- [[the system is the argument]] — philosophy with proof of work; every note, link, and MOC demonstrates the methodology it describes
10- [[coherent architecture emerges from wiki links spreading activation and small-world topology]] — the foundational triangle that answers what structure looks like, why it works, how agents navigate it, and when to reassess
11...
12
13## Topic MOCs
14
15The domain breaks into seven interconnected areas:
16
17- [[graph-structure]] — how wiki links, topology, and linking patterns create traversable knowledge graphs
18- [[agent-cognition]] — how agents think through external structures: traversal, sessions, attention limits
19  - [[agent-cognition-hooks]] — hook enforcement, composition, and cognitive science of automated quality
20  - [[agent-cognition-platforms]] — platform capability tiers, abstraction layers, context file architecture
21- [[discovery-retrieval]] — how descriptions, progressive disclosure, and search enable finding and loading content
22- [[processing-workflow]] — how throughput, sessions, and handoffs move work through the system
23...
24
25## Cross-Domain Claims
26
27- [[forced engagement produces weak connections]] — the social analog of accretion over productivity: forcing engagement for activity's sake produces shallow connections, just as accumulation without synthesis produces shallow knowledge graphs
28
29## Explorations Needed
30
31- Missing: comparison between human and agent traversal patterns. do agents need different architectures?
32- Scaling limits: at what system size does human curation fail?

the index isnt a lookup table its an entry point that points attention. the agent reads it, understands the landscape and follows the links that matter for the current conversation

each linked file is a standalone methodology claim (= skill). heres what one node looks like:

see how the wikilinks inside the prose tell the agent when and why to follow them

an map of contents (MOCs) organize sub-topics when the graph gets larger.

the evolution

skills are context engineering, basically curated knowledge injected where it matters

skill graphs are the next step

instead of one injection the agent navigates a knowledge structure, pulling in exactly what the current situation requires

this is the difference between an agent that follows instructions and an agent that understands a domain

arscontexta is a claude code plugin that does this for building knowledge systems. 249 files of structured knowledge the agent traverses to derive a local knowledge system that really fits your workflow

go use it and build skill graphs for everything else

heinrich