<h2 id="table-of-contents">Table of contents</h2>
<ul>
<li><a href="#a-warning-this-didnt-work-well">A warning: This didn't work well</a></li>
<li><a href="#why-an-org-chart-for-ai-agents">Why an org chart for AI agents</a></li>
<li><a href="#the-agents">The agents</a></li>
<li><a href="#the-org-chart">The org chart</a></li>
<li><a href="#pai-the-executive-assistant">Pai: the executive assistant</a>
<ul>
<li><a href="#agent-invocation-via-bash">Agent invocation via Bash</a></li>
</ul>
</li>
<li><a href="#the-wiki-layer">The wiki layer</a></li>
<li><a href="#pai-in-action">Pai in action</a>
<ul>
<li><a href="#the-quarterly-health-check">The quarterly health check</a></li>
</ul>
</li>
</ul>
<h1 id="a-warning-this-didnt-work-well">A warning: This didn't work well</h1>
<p>I found the process of building and testing this really fun. Trying to actually use it
afterwards was less fun.</p>
<ul>
<li><code>2026-03-11</code>: this is a "Hey this is neat" thing, not a  "this works really well!" one.</li>
<li><code>2026-03-15</code>: I've deleted basically all these agents and am down to 7.
The <a href="/using-agents-better.html">research-driven rebuild</a> applied Anthropic's
published agent design guidance to produce a leaner, better-spec'd roster.</li>
<li>I haven't given up on this idea but this approach was too ambitious and didn't have
the other infra in place it'd need to succeed.</li>
</ul>
<h1 id="why-an-org-chart-for-ai-agents">Why an org chart for AI agents</h1>
<p>Because it's cool, mostly. Also I'd hoped it would work really well.
But mostly, it's cool.</p>
<p>In this experiment I defined a team of Claude Code agents that each had a named role,
themed around being a virtual agent company. I figured I could run them manually or in
K8s using the Claude CLI:</p>
<pre><code class="language-bash">claude --agent cmo -p "What are my top 5 posts by pageviews?"
</code></pre>
<p>In theory I could manage agent context with this approach effectively and get each one
to stay in its own lane without bloating Claude's global context. CMO started with CMO
prompt, CDO with CDO prompt, so on.</p>
<p>The hope was to have these things each coordinate on their own, with my Pai agent acting
as a top-level orchestrator. They should have been able to use the wiki to exchange context.</p>
<p>I repurposed my OpenClaw agent
<a href="/openclaw-linear-skill.html">Pai</a>'s name when defining my orchestrator. I hoped to be
able to ask for things in Telegram and Pai would decompose the request into a
multi-agent flow and orchestrate from there.</p>
<p>The C-suite agents (CMO, CFO, CTO, CDO, CSO) each were envisioned as sub-orchestrators
that ran their own adversarial loops to keep standards high and sub-route requests.</p>
<h1 id="the-agents">The agents</h1>
<table>
<thead>
<tr>
<th>Agent</th>
<th>Role</th>
<th>Model</th>
<th>Key Tools</th>
</tr>
</thead>
<tbody>
<tr>
<td>Pai</td>
<td>Orchestration</td>
<td>Sonnet</td>
<td>Bash, Linear MCP</td>
</tr>
<tr>
<td>CMO</td>
<td>Traffic and growth</td>
<td>Sonnet</td>
<td>GA4 Analytics MCP</td>
</tr>
<tr>
<td>CFO</td>
<td>AI spend</td>
<td>Sonnet</td>
<td><a href="/openrouter-ai-tools.html">OpenRouter MCP</a></td>
</tr>
<tr>
<td>CTO</td>
<td>Delivery, blockers</td>
<td>Sonnet</td>
<td>Linear MCP, Bash</td>
</tr>
<tr>
<td>CDO</td>
<td>Knowledge management</td>
<td>Sonnet</td>
<td>Wiki read/write, Bash</td>
</tr>
<tr>
<td>CSO</td>
<td>Security and privacy</td>
<td>Sonnet</td>
<td>File tools, Bash</td>
</tr>
<tr>
<td>AR</td>
<td>Agent onboarding, mediation</td>
<td>Sonnet</td>
<td>File tools, Bash</td>
</tr>
<tr>
<td>Publisher</td>
<td>Blog content pipeline</td>
<td>Sonnet</td>
<td>Bash, file tools</td>
</tr>
<tr>
<td>SEO</td>
<td>Search audits</td>
<td>Sonnet</td>
<td>GA4, WebSearch</td>
</tr>
<tr>
<td>Cost Tracker</td>
<td>Spend reports</td>
<td>Haiku</td>
<td>OpenRouter MCP</td>
</tr>
<tr>
<td>Librarian</td>
<td>Wiki read/write</td>
<td>Haiku</td>
<td>Wiki file tools</td>
</tr>
<tr>
<td>Privacy Auditor</td>
<td>Flag confidential data</td>
<td>Haiku</td>
<td>File tools</td>
</tr>
<tr>
<td>Researcher</td>
<td>Gather sourced facts</td>
<td>Sonnet</td>
<td>WebSearch, Read</td>
</tr>
<tr>
<td>Writer</td>
<td>Draft posts from briefs</td>
<td>Sonnet</td>
<td>Read, Write</td>
</tr>
<tr>
<td>Fact Checker</td>
<td>Verify claims</td>
<td>Haiku</td>
<td>WebSearch, Read</td>
</tr>
<tr>
<td>Reviewer</td>
<td>Style and structure</td>
<td>Haiku</td>
<td>Read, Grep</td>
</tr>
<tr>
<td>QA</td>
<td>Production readiness</td>
<td>Sonnet</td>
<td>Bash, Playwright</td>
</tr>
</tbody>
</table>
<p>My only real use case at the time was writing blog posts, so the Publisher was special,
getting its own sub-org with a four-stage content
pipeline: researcher, writer, fact-checker, and reviewer.</p>
<p>QA ran after the pipeline to verify the post actually
built, rendered, and linked correctly before it went live.
QA used <a href="/playwright-mcp.html">Playwright MCP</a> to take screenshots
and inspect the rendered page.</p>
<p>Every agent connected to real
<a href="https://modelcontextprotocol.io/">MCP</a> servers to do cool stuff. The
CMO queried real <a href="/ga4-mcp.html">GA4 data</a>. The CFO pulled real OpenRouter
bills.</p>
<h1 id="the-org-chart">The org chart</h1>
<p>This was accurate as of 2026-03-12. The
<a href="/wiki/projects/agent-team/org-chart.html">bot-wiki org chart</a>
stayed up to date as agents were added or reorganized.</p>
<pre class="mermaid">
graph TD
    Kyle["Kyle"]
    Pai["Pai: Executive Assistant"]
    CMO["CMO: Grow Readership"]
    CFO["CFO: Optimize Spend"]
    CTO["CTO: Delivery"]
    CDO["CDO: Knowledge"]
    CSO["CSO: Security"]
    AR["AR: Agent Resources"]
    Publisher["Publisher: Content Pipeline"]

    SEO["SEO Subagent"]
    CostTracker["Cost Tracker"]
    Librarian["Librarian"]
    PrivacyAuditor["Privacy Auditor"]

    Researcher["Researcher"]
    Writer["Writer"]
    FactChecker["Fact Checker"]
    Reviewer["Reviewer"]
    QA["QA"]

    Kyle --> Pai
    Kyle --> CMO
    Kyle --> CFO
    Kyle --> CTO
    Kyle --> CDO
    Kyle --> CSO
    Kyle --> AR
    Kyle --> Publisher

    Pai -.->|orchestrates| CMO
    Pai -.->|orchestrates| CFO
    Pai -.->|orchestrates| CTO
    Pai -.->|orchestrates| CDO
    Pai -.->|orchestrates| CSO
    Pai -.->|orchestrates| AR
    Pai -.->|orchestrates| Publisher

    CMO --> SEO
    CFO --> CostTracker
    CDO --> Librarian
    CSO --> PrivacyAuditor

    CMO -.->|reads/writes| Librarian
    CFO -.->|reads/writes| Librarian
    CTO -.->|reads/writes| Librarian

    Publisher --> Researcher
    Publisher --> Writer
    Publisher --> FactChecker
    Publisher --> Reviewer
    Publisher --> QA
</pre>
<p>Every agent was directly
invocable by me, but ideally I didn't need to do that often.</p>
<p>Solid lines meant "reports to." Dashed lines from Pai meant
"orchestrates." Dashed lines to the Librarian meant "can
read/write wiki through."</p>
<p>The Privacy Auditor was the gatekeeper. Any agent writing
content that would end up in git or on the internet should
have checked with the Privacy Auditor first. It scanned for
leaked analytics data, spend numbers, secrets, and anything
else that shouldn't be public. This came from a scare where one of my agents with access
to mildly confidential data referenced it in content that
would have been public. I feel strongly that all agent flows
need a privacy step at minimum, and ideally agents should
have purposefully curtailed access to sensitive data.</p>
<p>The Librarian was an experiment in context management and
shared state. Any agent could talk to it directly to persist
notes, plans, or evidence to the wiki. I had already built a
<a href="/wiki-rag.html">RAG system</a> over the wiki. Once the wiki
got big enough that agents couldn't skim it all, I'd have
them query the RAG index instead. At that point, the agents
just used git and local file storage for wiki access. The CDO
owned the wiki strategy, but the Librarian did the actual
reading and writing.</p>
<h1 id="pai-the-executive-assistant">Pai: the executive assistant</h1>
<p>Agent definitions lived in <code>.claude/agents/</code>. Here was Pai's
frontmatter:</p>
<pre><code class="language-yaml"># .claude/agents/pai.md
name: pai
description: >-
  Pai — Executive assistant that orchestrates
  multi-agent workflows
model: sonnet
tools:
  - Bash
  - Read
  - Glob
  - Grep
  - Write
  - mcp__linear-server__list_issues
  - mcp__linear-server__list_projects
</code></pre>
<h2 id="agent-invocation-via-bash">Agent invocation via Bash</h2>
<p>Pai invoked other agents with <code>claude --agent &#x3C;name> -p "prompt"</code> through the Bash tool.
In theory I could have made an MCP for Pai that ran that in k8s pods for OpenClaw.
Each call was a fresh session. Agents didn't share memory or context with each other.</p>
<pre><code class="language-text">*Update*: The alternative I didn't try was just to use Claude Subagents for local work.
That's much easier and works better.
</code></pre>
<p>The alternatives I considered:</p>
<ul>
<li><strong>Shared memory / message bus.</strong> Tools like LangGraph or
CrewAI give agents shared state. More powerful, but more
moving parts. I wanted something I could debug by reading
a bash script.</li>
<li><strong>Single mega-agent.</strong> One agent definition with all the
tools. Simpler, but the context window filled up fast and
the agent lost focus. Splitting by role kept each
session lean.</li>
<li><strong>MCP-based orchestration.</strong> Route through an MCP server
that managed agent sessions. Interesting, but overkill
for a blog. I'd rather have built on <code>claude --agent</code>
which already worked.</li>
<li><strong><a href="https://github.com/steveyegge/beads">Beads</a>.</strong> A
structured task memory for AI agents, backed by Dolt
(version-controlled SQL). I looked into it, but it was
more of an alternative to Linear than to the wiki. It
solved task tracking and agent handoff, not knowledge
management. I liked Linear for tasks and markdown files
for knowledge.</li>
</ul>
<p>I went with the dumb approach: Bash calls and text passing.
It was easy to understand, easy to debug, and the wiki
handled long-term memory. If I outgrew it, I'd upgrade.</p>
<h1 id="the-wiki-layer">The wiki layer</h1>
<p>Each agent had a page in the
<a href="/bot-wiki.html">Bot-Wiki</a> that documented its goal, tools,
subagents, and example prompts.</p>
<pre><code class="language-text">agent-team/
├── index.md          # org chart and coordination model
├── pai.md            # orchestration agent
├── cmo.md            # traffic and growth
├── cfo.md            # AI spend
├── cto.md            # delivery and blockers
├── cdo.md            # knowledge management
├── cso.md            # security and privacy
├── publisher.md      # blog pipeline
└── phase-2.md        # future async architecture
</code></pre>
<p>Every agent session was stateless. The CMO didn't remember
what the CTO said last week. But if the CTO wrote its
findings to the wiki through the Librarian, the CMO could
read them next time it ran. The wiki was how agents shared
context across sessions.</p>
<p>The Librarian (a Haiku subagent under the CDO) handled
all wiki read/write operations. Any agent could invoke it
directly with <code>claude --agent librarian -p "..."</code> to
persist notes, plans, evidence, or whatever else needed to
survive between sessions.</p>
<p>The CDO owned the strategy: what got documented, how pages
were structured, when content was stale. This was cheaper
than giving every agent write access to the full file
system. The Librarian ran on Haiku, knew the wiki format,
and wouldn't accidentally clobber unrelated files.</p>
<p>In time I would have had the CFO tweak the models the
agents used to manage costs, too.</p>
<h1 id="pai-in-action">Pai in action</h1>
<h2 id="the-quarterly-health-check">The quarterly health check</h2>
<p>I asked Pai for a quarterly health check. It invoked three
agents and synthesized everything into one report.</p>
<p><strong>Traffic</strong> (from CMO): GA4 only had a week of data. The
site was new enough that there was no meaningful baseline yet.
Almost entirely direct traffic, organic search barely
registering. CMO recommended connecting Search Console and
revisiting in 30 days.</p>
<p><strong>AI spend</strong> (from CFO): well under budget with no pressure.
OpenRouter's API only surfaced account-level totals, no
per-model breakdown. CFO noted the biggest future lever was
routing non-reasoning tasks to flash-tier models.</p>
<p><strong>Delivery</strong> (from CTO): most issues done or in backlog,
one in progress (this blog post). No hard blockers, but
three dependency chains worth watching:</p>
<ol>
<li>RSS feed was high priority but hadn't started. Four
distribution issues were gated on it.</li>
<li>CMO and CFO baseline runs hadn't started. They gated
two future blog posts.</li>
<li>This post had an open PR to close.</li>
</ol>
<p>Pai wrote a log entry automatically:</p>
<pre><code class="language-markdown">## 21:30 — Quarterly health check

| Agent | Prompt Summary | Result |
|-------|---------------|--------|
| cmo | 90-day traffic trend | success |
| cfo | 90-day AI spend | success |
| cto | Blocked/stalled issues | success: 3 clusters |

**Synthesis:** Early launch phase, minimal spend,
no hard blockers but three dependency chains.
</code></pre>
<p>That version of Pai ran well for a few weeks. Eventually I moved away from
OpenClaw entirely and <a href="/openclaw-inspired-assistant.html">rebuilt it from scratch</a>,
keeping the name but replacing almost everything under the hood.</p>


<p>The <a href="/bot-wiki.html">bot-wiki post</a> introduced a structured wiki
designed for machine consumption. The whole point was to build
something a RAG pipeline could actually use. This is that pipeline.</p>
<h2 id="the-stack">The stack</h2>
<p>The wiki has 21 markdown pages with structured frontmatter:
title, summary, keywords, scope. The RAG pipeline is a single
Python script that:</p>
<ol>
<li>Embeds all 21 pages into vectors using OpenRouter's API</li>
<li>Stores them in a FAISS flat index (129 KB, committed to git)</li>
<li>At query time, embeds the question, finds the nearest pages,
and feeds them to an LLM for a grounded answer</li>
</ol>
<p>Dependencies: <code>faiss-cpu</code>, <code>openai</code> (for the OpenAI-compatible
API), and <code>pyyaml</code>. That's it.</p>
<h2 id="why-faiss">Why FAISS</h2>
<p><a href="https://github.com/facebookresearch/faiss">FAISS</a> is Meta's
library for similarity search over dense vectors. It runs
in-process, no server needed. You give it vectors, it gives
you nearest neighbors. The index is a flat file you can
commit to git.</p>
<p>I picked it because this is 21 vectors. There's no need for
a database, a server, or a managed service. <code>pip install faiss-cpu</code>, build an index, save it, done. The index is
129 KB. It loads in milliseconds.</p>
<p>If you're working at larger scale or want more features,
the main alternatives are:</p>
<ul>
<li><a href="https://github.com/chroma-core/chroma">Chroma</a>: embedded
vector database with a nice Python API. Good if you want
metadata filtering or persistence without managing files
yourself. Still runs locally.</li>
<li><a href="https://github.com/qdrant/qdrant">Qdrant</a>: vector search
engine written in Rust. Runs as a server, supports
filtering and payloads. Better fit when you have millions
of vectors or need a proper API.</li>
<li><a href="https://www.pinecone.io/">Pinecone</a>: fully managed cloud
vector database. Zero ops, but you're paying per query and
your data lives on someone else's servers.</li>
</ul>
<p>If you're already running PostgreSQL,
<a href="https://github.com/pgvector/pgvector">pgvector</a> is worth
a look. It adds vector columns and similarity search to
your existing database. No new service to run, and your
vectors live next to your relational data.</p>
<p>For 21 pages committed to a git repo, FAISS is the obvious
choice. No dependencies beyond the pip package, no running
services, no accounts.</p>
<h2 id="why-whole-page-chunks">Why whole-page chunks</h2>
<p>The wiki pages are short, 18 to 65 lines each. Splitting them
by section would lose context without gaining anything. The
frontmatter was designed to enrich embeddings, so each chunk
looks like this:</p>
<pre><code>Title: Claude Code
Summary: Anthropic's CLI-based AI coding assistant. Runs
  in terminal, edits files directly, supports MCP tool
  integrations and custom hooks.
Keywords: claude-code, anthropic, cli, ai-coding-assistant,
  mcp, hooks
Scope: Covers Claude Code setup, configuration, MCP
  integration, hooks, and usage patterns.

Claude Code is Anthropic's CLI tool for AI-assisted software
engineering. It operates directly in the terminal...
</code></pre>
<p>Prepending the summary, keywords, and scope to the body gives
the embedding model more signal about what the page covers.
21 chunks, 5,434 tokens total.</p>
<h3 id="chunking-in-general">Chunking in general</h3>
<p>Most RAG guides recommend chunks of 256 to 512 tokens. That's
a reasonable default for long documents where you want to
retrieve a specific paragraph, not the whole thing. Smaller
chunks give more precise retrieval. Larger chunks give the
LLM more context per result.</p>
<p>The tradeoffs:</p>
<ul>
<li><strong>Too small</strong> (under 100 tokens): chunks lose meaning on
their own. "See above" or "as mentioned" with no referent.
Retrieval finds fragments, not answers.</li>
<li><strong>Too large</strong> (over 1000 tokens): embeddings become diluted.
A chunk about five topics matches all five weakly instead
of one strongly. You also burn more of the LLM's context
window per retrieved result.</li>
<li><strong>Overlap</strong> helps when chunks split mid-thought. A common
pattern is 512-token chunks with 50-100 tokens of overlap.
This adds redundancy but reduces the chance of cutting a
relevant passage in half.</li>
</ul>
<p>The wiki pages average about 260 tokens each, which lands
right in the sweet spot. They're also self-contained by
design, each page covers one topic with clear boundaries.
No reason to split them further.</p>
<p>If I were indexing longer documents (blog posts, docs, book
chapters), I'd split by section headers first, then by token
count within sections. Semantic boundaries beat arbitrary
ones.</p>
<h2 id="building-the-index">Building the index</h2>
<pre><code class="language-bash">python3 -m venv .venv
.venv/bin/pip install -r bin/requirements-rag.txt
.venv/bin/python bin/wiki-rag.py build
</code></pre>
<pre><code>Embedding 21 wiki pages...
Indexed 21 pages, 1536 dimensions, 5434 tokens
Index: 129,069 bytes, metadata: 29,663 bytes
Saved to blog/markdown/wiki/.index
</code></pre>
<p>The embedding model is <code>text-embedding-3-small</code> via OpenRouter.
1536 dimensions per vector. The FAISS index uses <code>IndexFlatIP</code>
(inner product on normalized vectors = cosine similarity). No
approximate search needed at this scale.</p>
<p>The index and a <code>metadata.json</code> file (slug, title, summary,
full chunk text for each page) get saved to
<code>blog/markdown/wiki/.index/</code>. Both files are committed to git.
Any tool that wants to query the wiki can just read the index
directly without needing the embedding API.</p>
<p>The build is a manual step, not part of <code>npm run build</code>. Wiki
content doesn't change often, and there's no reason to pay for
embeddings on every deploy.</p>
<h2 id="querying">Querying</h2>
<p>A query hits two models. First, the question gets embedded
into a vector using the same embedding model that indexed
the wiki pages. FAISS compares that vector against the 21
stored vectors and returns the closest matches. Those
matches are wiki pages that are semantically similar to the
question, not keyword matches, but meaning matches.</p>
<p>Then the retrieved pages get passed to an LLM (Claude
Sonnet) as context, along with the original question. The
LLM reads the pages and writes an answer grounded in what
they actually say. Without the retrieval step, the LLM
would just guess from its training data. With it, the LLM
answers from your specific documents.</p>
<p>That's the "retrieval-augmented" part of RAG: find relevant
documents first, then generate an answer from them.</p>
<pre class="mermaid">
graph LR
    Q[Question] --> E[Embed question]
    E --> F[FAISS search]
    F --> R[Top 3 wiki pages]
    R --> P[Build prompt]
    P --> LLM[Claude Sonnet]
    LLM --> A[Answer + sources]
</pre>
<pre><code class="language-bash">.venv/bin/python bin/wiki-rag.py query \
  "What MCP servers does this project use?"
</code></pre>
<pre><code>Question: What MCP servers does this project use?

Top 3 matches:
  1. [0.644] MCP Integrations (wiki/mcp)
  2. [0.563] Linear MCP (wiki/mcp/linear)
  3. [0.524] GA4 MCP (wiki/mcp/ga4)

Answer:
Based on the provided context, this project uses three MCP
servers:

1. Playwright, for browser automation
2. Linear, for project management
3. GA4, for accessing website traffic data and reports

All MCP servers run as subprocesses communicating via JSON-RPC
over stdio, and are configured in ~/.claude.json or a
project-level .claude.json file.
</code></pre>
<p>The retrieval nailed the right pages. Here's a different query:</p>
<pre><code class="language-bash">.venv/bin/python bin/wiki-rag.py query "How do I run security scans?"
</code></pre>
<pre><code>Question: How do I run security scans?

Top 3 matches:
  1. [0.484] Security Toolkit (wiki/devops/security-toolkit)
  2. [0.439] DevOps &#x26; Security (wiki/devops)
  3. [0.343] OpenClaw Kubernetes Security (wiki/openclaw/kubernetes)
</code></pre>
<p>The answer included the actual docker commands from the security
toolkit wiki page, copy-paste ready. The third result
(Kubernetes security) is a reasonable stretch, the word
"security" overlaps, but the top two are exactly right.</p>
<h2 id="the-script">The script</h2>
<p>The interesting parts are small. Embedding is a single API call
since all 21 pages fit in one batch:</p>
<pre><code class="language-python">client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
)
response = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input=texts,
)
</code></pre>
<p>OpenRouter's embedding endpoint is OpenAI-compatible, so the
<code>openai</code> Python package works with just a base URL swap.</p>
<p>FAISS search returns similarity scores and indices:</p>
<pre><code class="language-python">scores, indices = index.search(query_vec, k)
for score, idx in zip(scores[0], indices[0]):
    page = metadata[idx]
    # score is cosine similarity (0 to 1)
</code></pre>
<p>The retrieved pages get stuffed into a prompt with instructions
to only use the provided context and cite sources. The LLM
(Claude Sonnet via OpenRouter) generates the answer.</p>
<h2 id="cost">Cost</h2>
<p>Indexing 21 pages used 5,434 tokens at
<code>text-embedding-3-small</code> rates ($0.02/1M tokens). That's
$0.0001. Each query embeds a short question (maybe 20 tokens)
plus one chat completion. A query costs roughly $0.002 to
$0.01 depending on the response length.</p>
<p>Building the index and running test queries cost $0.03
total on OpenRouter. That covers the embedding API calls
and the chat completions for query-mode answers. The
development itself (writing the script, iterating on the
blog post) ran through Claude Code on an Anthropic Max
subscription, so that cost isn't on the OpenRouter bill.</p>
<p>The wiki is also a write target, not just a read one. I have
a journalist agent that runs on a K8s CronJob every morning,
searches for AI news, and commits a digest to the wiki automatically.
<a href="/cron-event-triggered-ai-agents-k8s.html">Cron/event-triggered AI agents on K8s</a>
covers how that side works.</p>


<p>I added a new top-level section to this site: the
<a href="/wiki.html">Bot-Wiki</a>.</p>
<p>It looks like a wiki. It reads like a wiki. But it's not really for
humans. It's a structured knowledge base designed for AI agents and
RAG pipelines to consume.</p>
<h2 id="why-a-wiki-when-i-already-have-a-blog">Why a wiki when I already have a blog?</h2>
<p>Blog posts are stories. They have a narrative arc, opinions, tangents.
They're written for people who want to understand how I got from point
A to point B. That's great for learning from my experience but
terrible for a robot trying to answer "what MCP servers does this
project use?"</p>
<p>The wiki is the opposite. Each page is a factual reference card.
Structured metadata, explicit scope declarations, keyword lists for
embedding search, and cross-references for graph traversal. The tone
is robots writing notes for other robots.</p>
<h2 id="whats-in-it">What's in it</h2>
<p>The wiki covers the same ground as my recent blog posts but organized
by topic rather than chronology:</p>
<ul>
<li><strong>AI Tools</strong>: Claude Code, OpenCode, OpenRouter, local LLMs, image gen</li>
<li><strong>MCP Integrations</strong>: Playwright, Linear, GA4</li>
<li><strong>OpenClaw</strong>: the agent, its skills, Kubernetes security</li>
<li><strong>DevOps</strong>: security toolkit, lint toolkit, Ruler</li>
<li><strong>Blog Architecture</strong>: how this site is built, the markdown pipeline,
testing setup</li>
</ul>
<p>Each page has frontmatter designed for machine consumption:</p>
<pre><code class="language-yaml">title: "Claude Code"
summary: "CLI AI coding assistant by Anthropic with MCP support"
keywords:
  - claude-code
  - anthropic
  - mcp
related:
  - wiki/ai-tools/opencode
  - playwright-mcp
scope: "Setup, config, MCP integration, and usage patterns"
last_verified: 2026-03-10
</code></pre>
<p>The <code>summary</code> and <code>scope</code> fields help a RAG system decide whether to
retrieve the full page. The <code>keywords</code> list feeds embedding search.
The <code>related</code> links let an agent traverse the knowledge graph.
<code>last_verified</code> tells the agent how stale the content might be.</p>
<h2 id="how-this-connects-to-rag">How this connects to RAG</h2>
<p>This is step one of a larger plan. Next I'm building a RAG pipeline
that indexes the wiki content, generates embeddings, and lets agents
query it during conversations. The wiki's structured metadata makes
chunking and retrieval much cleaner than trying to RAG over blog
posts directly.</p>
<p>The directory structure maps to the URL structure which maps to the
knowledge hierarchy. An agent asking about MCP servers gets pointed
to <code>wiki/mcp/</code> and its children, not to a 2000-word blog post that
mentions MCP in passing.</p>
<h2 id="how-its-built">How it's built</h2>
<p>The wiki reuses the blog's existing infrastructure. A new <code>WikiService</code>
reads markdown from <code>markdown/wiki/</code> instead of <code>markdown/posts/</code>,
builds a tree from the directory structure, and generates navigation
automatically. Each <code>index.md</code> file gets a tree of its children
rendered above its own content.</p>
<p>The routing plugs into the same Next.js catch-all that handles blog
posts. Wiki pages get the same layout, sidebar, and responsive
behavior. They just have different content and metadata.</p>
<p>No images in the wiki. It's all text, all the time. Robots don't
need hero images.</p>
<p>You can find it at <a href="/wiki.html">/wiki.html</a>, or use the new Wiki
link in the header.</p>


Kyle Pericak

Attempting an AI Agent Org Chart

RAG on the Bot-Wiki

Bot-Wiki: A Knowledge Base for Robots