<h2 id="table-of-contents">Table of contents</h2>
<ul>
<li><a href="#why-i-dont-trust-the-skill-marketplace">Why I don't trust the skill marketplace</a></li>
<li><a href="#how-openclaw-skills-work">How OpenClaw skills work</a></li>
<li><a href="#the-skill">The skill</a></li>
<li><a href="#one-ai-writing-tools-for-another">One AI writing tools for another</a></li>
<li><a href="#loading-it-into-k8s">Loading it into K8s</a>
<ul>
<li><a href="#injecting-the-api-key">Injecting the API key</a></li>
<li><a href="#mounting-the-skill">Mounting the skill</a></li>
<li><a href="#the-jq-problem">The jq problem</a></li>
<li><a href="#stale-sessions">Stale sessions</a></li>
</ul>
</li>
<li><a href="#talking-to-pai-pericak-ai">Talking to Pai (Pericak AI)</a></li>
</ul>
<h1 id="why-i-dont-trust-the-skill-marketplace">Why I don't trust the skill marketplace</h1>
<p>In the <a href="/openclaw-k8s.html">K8s post</a> I mentioned wanting
to connect OpenClaw to Linear for issue tracking. The
obvious path is ClawHub, OpenClaw's public skill
marketplace. Install a community skill, set your API key,
done. I didn't do that.</p>
<p>In February 2026, a campaign called
<a href="https://cybersecuritynews.com/clawhavoc-poisoned-openclaws-clawhub/">ClawHavoc</a>
compromised ClawHub. Researchers at Trend Micro and Antiy
documented over 1,184 malicious skills that had been
published to the registry. The skills looked legitimate,
had normal names and descriptions, and passed ClawHub's
automated checks. They distributed
<a href="https://www.trendmicro.com/en_us/research/26/b/openclaw-skills-used-to-distribute-atomic-macos-stealer.html">Atomic macOS Stealer</a>,
a credential harvester targeting Keychain, browser
passwords, and crypto wallets.</p>
<p>The publishing bar was low. A one-week-old GitHub account
was enough to push a skill to ClawHub. No code review, no
signing, no sandbox. Once installed, a skill runs inside
the agent process with access to every environment variable
the agent has. That includes API keys, tokens, and anything
else injected via Vault or config.</p>
<p>This is the same pattern that hit npm, PyPI, and VS Code
extensions. A package registry with low friction publishing
and high-privilege execution is a target. ClawHub is no
different.</p>
<p>My approach: write the skill myself, keep it in my repo,
and audit every line. The Linear skill I need is about 150
lines of markdown. That's a small surface to own compared
to trusting a stranger's code with my credentials.</p>
<h1 id="how-openclaw-skills-work">How OpenClaw skills work</h1>
<p>An OpenClaw skill is a directory with a <code>SKILL.md</code> file.
The file has YAML frontmatter (name, description, required
tools and environment variables) and a markdown body with
instructions the agent follows.</p>
<p>There's no compiled code. No dependencies to install. No
background process. The agent reads the markdown, learns
what commands are available, and executes them via bash
when you ask it to. If you want to audit a skill, you read
one file.</p>
<p>Compare this to MCP servers, which are running processes
that the agent connects to over a transport protocol. MCP
servers can do more (stateful connections, streaming), but
they're also harder to audit and have a larger attack
surface. For something like "list my Linear issues," a
static document with curl commands is plenty.</p>
<p>The frontmatter declares what the skill needs:</p>
<pre><code class="language-yaml">---
name: linear
description: Manage Linear issues, projects,
  and comments via the Linear GraphQL API.
metadata: {"openclaw": {"emoji": "📐",
  "requires": {"bins": ["curl"],
  "env": ["LINEAR_API_KEY"]}}}
---
</code></pre>
<p>OpenClaw checks these requirements at load time. If <code>curl</code>
isn't on PATH, or <code>LINEAR_API_KEY</code> isn't set, the skill
doesn't load. No runtime errors, no mysterious failures.</p>
<h1 id="the-skill">The skill</h1>
<p>I had Claude write a Linear skill covering the operations
I actually use: listing issues, creating and updating them,
managing labels, and adding comments. The full skill lives
at <code>apps/openclaw-skills/linear/SKILL.md</code> in my monorepo.</p>
<p>Linear's API is GraphQL-only. No REST endpoints. Every
request is a POST to <code>https://api.linear.app/graphql</code> with
a JSON body containing the query. The skill uses <code>curl</code>
for HTTP. One API key, no OAuth dance.</p>
<p>The auth header is <code>Authorization: $LINEAR_API_KEY</code>.
Linear personal API keys don't use a <code>Bearer</code> prefix.</p>
<p>Here's what listing issues looks like:</p>
<pre><code class="language-bash">curl -s -X POST https://api.linear.app/graphql \
  -H "Content-Type: application/json" \
  -H "Authorization: $LINEAR_API_KEY" \
  -d '{"query": "{ issues(filter: { team: \
    { key: { eq: \"PER\" } } }, first: 25) \
    { nodes { id identifier title \
    state { name } } } }"}'
</code></pre>
<p>And creating an issue:</p>
<pre><code class="language-bash">curl -s -X POST https://api.linear.app/graphql \
  -H "Content-Type: application/json" \
  -H "Authorization: $LINEAR_API_KEY" \
  -d '{"query": "mutation { issueCreate(input: \
    { teamId: \"TEAM_UUID\", \
    title: \"Issue title\", \
    description: \"Issue description\" }) \
    { success issue { id identifier url } } }"}'
</code></pre>
<p>The skill also covers workflow statuses. Changing an
issue's status in Linear means looking up the target
state's UUID first, then passing it to <code>issueUpdate</code>. The
agent chains these naturally: list statuses, find the one
named "Todo", grab its ID, update the issue.</p>
<p>This matches the workflow I described in the
<a href="/linear-mcp.html">Linear MCP post</a>. Nightly agents create
Suggestion issues in Backlog, I promote the good ones to
Todo, working agents pick them up. The skill gives OpenClaw
the same Linear access that Claude Code gets through its
MCP server.</p>
<p>The full skill is about 150 lines of markdown. Ten
operations with copy-paste curl commands and a few compound
examples at the end. I read every line.</p>
<h1 id="one-ai-writing-tools-for-another">One AI writing tools for another</h1>
<p>Claude Code (Opus 4.6) wrote this skill. It read the
Trello skill as a pattern, checked the Linear GraphQL API
docs, and produced a skill that follows the same structure.
One AI building capabilities for another AI.</p>
<p>I described what I wanted at a high level, and Claude
handled the GraphQL query syntax, the jq filters, and the
escape sequences. I reviewed the output. The whole thing
took one conversation.</p>
<p>The skill lives in my monorepo under version control. If
Linear changes their API or I need a new operation, I
update one file.</p>
<h1 id="loading-it-into-k8s">Loading it into K8s</h1>
<p>Getting the skill into the running OpenClaw pod took more
work than writing it. The skill file needs to end up in
the right directory inside the container, the API key
needs to be injected, and OpenClaw needs to discover it.</p>
<h2 id="injecting-the-api-key">Injecting the API key</h2>
<p>I added the Linear API key to Vault alongside the existing
Gemini and Telegram secrets, then updated the Vault Agent
template to export it:</p>
<pre><code class="language-text">{{- with secret "secret/openclaw" -}}
export GEMINI_API_KEY="{{ .Data.data.gemini_api_key }}"
export TELEGRAM_BOT_TOKEN="..."
{{ if .Data.data.linear_api_key }}
export LINEAR_API_KEY="{{ .Data.data.linear_api_key }}"
{{ end }}
{{- end }}
</code></pre>
<h2 id="mounting-the-skill">Mounting the skill</h2>
<p>I stored the SKILL.md in a ConfigMap and mounted it into
the init container. ConfigMap mounts use symlinks
internally, and OpenClaw's skill loader rejects symlinked
paths as a security measure. So the init container copies
the file onto the PVC as a regular file:</p>
<pre><code class="language-yaml">initContainers:
  - name: config-seed
    command: ["sh", "-c"]
    args:
      - |
        # ... existing config seed logic ...
        # Copy skills onto PVC workspace
        mkdir -p /data/workspace/skills/linear
        cp /skill-src/linear/SKILL.md \
          /data/workspace/skills/linear/SKILL.md
    volumeMounts:
      - name: linear-skill
        mountPath: /skill-src/linear
        readOnly: true
</code></pre>
<p>The key detail: skills must live under the workspace
directory (<code>&#x3C;workspace>/skills/</code>), not just anywhere on
the filesystem. OpenClaw's skill discovery only scans
three locations: bundled, managed (<code>~/.openclaw/skills</code>),
and workspace. I initially tried <code>extraDirs</code> and the
managed path. Neither worked until I put it in the
workspace.</p>
<h2 id="the-jq-problem">The jq problem</h2>
<p>The original skill required both <code>curl</code> and <code>jq</code> in its
<code>requires.bins</code> frontmatter. The OpenClaw container has
<code>curl</code> but not <code>jq</code>. The skill gating check runs at
startup: missing binary means the skill silently doesn't
load. Claude dropped <code>jq</code> from the requirements. The agent
is smart enough to parse JSON responses on its own using
the <code>node</code> binary that's already in the container.</p>
<h2 id="stale-sessions">Stale sessions</h2>
<p>After all that, I asked Pai what skills it had. Still
three. OpenClaw snapshots eligible skills when a session
starts and reuses that list for the session's lifetime.
The <code>openclaw agent --message</code> CLI command was reusing an
existing session from before the skill was loaded.
Clearing the sessions directory forced a fresh snapshot:</p>
<pre><code class="language-bash">rm -rf /home/node/.openclaw/agents/main/sessions/*
</code></pre>
<p>After that, the skill appeared.</p>
<h1 id="talking-to-pai-pericak-ai">Talking to Pai (Pericak AI)</h1>
<p>With the skill loaded, Claude Code talked to Pai (the
OpenClaw agent running Gemini 2.5 Flash) through
<code>kubectl exec</code>, using the same pattern from the
<a href="/openclaw-k8s.html">K8s post</a>.</p>
<pre><code class="language-text">Claude: List my Linear teams using the linear skill.
Pai: Your Linear teams are:
  - Pericak (Key: PER)

Claude: List my Linear issues for team PER.
Pai: Here are the Linear issues for team PER:
  - PER-10: ... (State: Backlog)
  - PER-9: ... (State: Todo)
  - PER-8: ... (State: Todo)
  - PER-7: ... (State: Todo)
  - PER-6: ... (State: Todo)
  - PER-5: ... (State: Todo)
  ...
</code></pre>
<p>That's real output (titles redacted). Pai read the
skill's curl commands, called the Linear GraphQL API
with the Vault-injected API key, and returned my actual
issues. The full chain: Claude Code runs <code>kubectl exec</code>,
which calls the <code>openclaw agent</code> CLI, which sends the
message to Gemini 2.5 Flash, which reads the skill, runs
curl against Linear's API, and returns the result.</p>
<p>Two AIs, two different models, coordinating through a
Kubernetes pod with Vault-injected secrets and a
hand-written skill file. The whole thing is auditable
from end to end.</p>
<p>After getting comfortable with how OpenClaw works, I eventually
dropped it and <a href="/openclaw-inspired-assistant.html">built my own assistant from scratch</a>.
Same core ideas, narrower scope, and code I actually understand.</p>


<h1 id="what-openclaw-is">What OpenClaw is</h1>
<p><a href="https://openclaw.ai/">OpenClaw</a> is wildly popular and kind of confusing. From what I
can tell before starting, other than being a first-class security concern, it's meant
to be a tool that lets you connect to your ai agents over chat tools like WhatsApp.</p>
<p>It runs locally, you bring your own API key (Anthropic, OpenAI,
DeepSeek), and your data stays on your machine (until you screw up). The gateway
connects to messaging platforms like Telegram, Signal, WhatsApp,
Discord, and apparently about 20 others.</p>
<h1 id="install-on-macos">Install on macOS</h1>
<p>Prerequisites: Node.js 22+ and an API key for your chosen
model provider (more on that next).</p>
<p>The recommended install uses their script:</p>
<pre><code class="language-bash">curl -fsSL https://openclaw.ai/install.sh | bash
</code></pre>
<p>That handles Node detection and installs the <code>openclaw</code> CLI.
If you want to skip the wizard and install the binary only:</p>
<pre><code class="language-bash">curl -fsSL https://openclaw.ai/install.sh | bash -s -- --no-onboard
</code></pre>
<p>Then run the onboarding wizard separately:</p>
<pre><code class="language-bash">openclaw onboard
</code></pre>
<p>The wizard walks through gateway setup, workspace directory,
and API key config. Everything lives in <code>~/.openclaw/</code>.</p>
<h1 id="picking-a-model">Picking a model</h1>
<p>The onboard wizard asks which model to use. OpenClaw supports
a long list of providers, but here are the ones that matter
for a personal assistant:</p>
<table>
<thead>
<tr>
<th>Model</th>
<th>Input / Output (per 1M tokens)</th>
<th>Good for</th>
</tr>
</thead>
<tbody>
<tr>
<td>Claude Haiku 4.5</td>
<td>$1 / $5</td>
<td>Cheap daily driver</td>
</tr>
<tr>
<td>Claude Sonnet 4.6</td>
<td>$3 / $15</td>
<td>Solid middle ground</td>
</tr>
<tr>
<td>Claude Opus 4.6</td>
<td>$5 / $25</td>
<td>Best reasoning</td>
</tr>
<tr>
<td>GPT-5</td>
<td>$1.25 / $10</td>
<td>Strong alternative</td>
</tr>
<tr>
<td>GPT-4o Mini</td>
<td>$0.15 / $0.60</td>
<td>Budget OpenAI</td>
</tr>
<tr>
<td>Gemini 2.5 Flash</td>
<td>Free (250 req/day) / ~$0.30 paid</td>
<td>Free option</td>
</tr>
<tr>
<td>Gemini 3 Pro</td>
<td>$2 / $12</td>
<td>Strong reasoning</td>
</tr>
<tr>
<td>DeepSeek (via Ollama)</td>
<td>Free (local)</td>
<td>Fully offline</td>
</tr>
</tbody>
</table>
<h2 id="can-you-use-your-20month-subscription">Can you use your $20/month subscription?</h2>
<p><strong>ChatGPT Plus/Pro:</strong> No. The subscription doesn't include API
access. You need a separate OpenAI API key with pay-as-you-go
billing.</p>
<p><strong>Claude Pro ($20/mo):</strong> Not anymore. Early on, people found a
workaround using OAuth tokens from the Claude Code CLI.
Anthropic shut that down in January 2026. You need a separate
Anthropic API key.</p>
<p><strong>Claude Max ($200/mo) and Claude Code subscription:</strong> Same
story. The setup-token auth path existed briefly but Anthropic
scoped the tokens so they only work with the official Claude
Code client.</p>
<h2 id="trying-geminis-model">Trying Gemini's model</h2>
<p>Apparently Google's Gemini API has a free tier, I tried it out. Gemini 2.5 Flash gets
250 requests/day free. For texting a bot a few times a day to manage issues, that's ok.</p>
<p>Annoyingly there are a bunch of 2.5 options. No idea if they all have free tiers.</p>
<p>I initially picked <code>google/gemini-2.5-flash-preview-09-2025</code> during the wizard and
gave it my api key using the <code>Paste API key now</code> option. Sadly that had an error</p>
<pre><code class="language-test">models/gemini-2.5-flash-preview-09-2025 is not found for API version v1beta,
</code></pre>
<p>Fixing it was a pain in the ass, their <code>/model</code> and <code>/models</code> tools TUI setup was jank
af and just didnt work.</p>
<pre><code class="language-bash">/exit
openclaw configure
</code></pre>
<p>I tried to switch the model up to <code>google/gemini-2.5-flash</code>.</p>
<p>Note that this thing stays running in the background</p>
<pre><code class="language-bash">➜  ~ ps aux | grep openclaw
kp               39009   0.1  1.0 444980720 173456   ??  R    11:03AM   0:07.97 openclaw-gateway
</code></pre>
<p>Re-open the TUI with <code>openclaw tui</code> if you end up out of it.</p>
<h2 id="connect-telegram">Connect Telegram</h2>
<p>I paused here to install Telegram on my MacBook. It's pretty easy if you already have
it on your phone, you just scan a QR code.</p>
<p>During the quick start it recommends you use Telegram. I chose to use
<code>Telegram (Bot API)</code>. Then I followed the instructions:</p>
<pre><code class="language-text">◇  Telegram bot token ───────────────────────────────────────────────────────────────────╮
│                                                                                        │
│  1) Open Telegram and chat with @BotFather                                             │
│  2) Run /newbot (or /mybots)                                                           │
│  3) Copy the token (looks like 123456:ABC...)                                          │
│  Tip: you can also set TELEGRAM_BOT_TOKEN in your env.                                 │
│  Docs: https://docs.openclaw.ai/telegram  │
│  Website: https://openclaw.ai                                                          │
│                                                                                        │
├────────────────────────────────────────────────────────────────────────────────────────╯
</code></pre>
<p>The /newbot prompt had me name it. Then I got the key to give OpenClaw.</p>
<h2 id="set-up-search">Set up Search</h2>
<p>I chose to use Gemini since I'm giving it that API key anyways. Plus, Google's gotta be
the best at search, right? It's like, their thing. Suprisingly it asked again for
<code>Gemini (Google Search) API key</code>. I just gave it the same key as before.</p>
<h2 id="sketchy-skills-time">Sketchy Skills Time</h2>
<p>Then I got to here and just proceeed. There was some news about malware in the skills.</p>
<pre><code class="language-text">◇  Skills status ─────────────╮
│                             │
│  Eligible: 8                │
│  Missing requirements: 43   │
│  Unsupported on this OS: 0  │
│  Blocked by allowlist: 0    │
│                             │
├─────────────────────────────╯
</code></pre>
<h2 id="places-api">Places API</h2>
<p>What, why?</p>
<pre><code class="language-text">◆  Set GOOGLE_PLACES_API_KEY for goplaces?
│  ○ Yes / ● No
</code></pre>
<h2 id="gemini-for-nano-banana">Gemini for nano banana</h2>
<p>I mean yeah but I already gave it this key twice, kinda annoying.</p>
<pre><code class="language-text">◆  Set GEMINI_API_KEY for nano-banana-pro?
│  ● Yes / ○ No
</code></pre>
<h2 id="the-other-keys-it-wants">The other keys it wants</h2>
<p>... It wants a lot of keys, just start saying no.</p>
<h2 id="hooks">Hooks</h2>
<p>I don't trust boot-md yet.</p>
<pre><code class="language-text">◆  Enable hooks?
│  ◻ Skip for now
│  ◻ 🚀 boot-md
│  ◼ 📎 bootstrap-extra-files (Inject additional workspace bootstrap files via glob/path patterns)
│  ◼ 📝 command-logger (Log all command events to a centralized audit file)
│  ◼ 💾 session-memory (Save session context to memory when /new or /reset command is issued)
</code></pre>
<h2 id="node-wants-to-find-devices-on-local-networks">Node wants to find devices on local networks</h2>
<p>MacOS popup. Yeah, nah. This is already probably the least secure thing I've ever done
on purpose.</p>
<h2 id="hatch-the-bot">Hatch the bot</h2>
<p>I love a good TUI.</p>
<pre><code class="language-text">◆  How do you want to hatch your bot?
│  ● Hatch in TUI (recommended)
│  ○ Open the Web UI
│  ○ Do this later
</code></pre>
<h1 id="talk-to-the-bot-on-telegram">Talk to the bot on Telegram</h1>
<p>The BotFather sends you a link to your bot account. I clicked it, hit start, and that
sent /start to it. It immediately told me OpenClaw: access not configured. Love it.
I copied its whole message and just dumped it into the TUI for OC to sort it out.</p>
<p>Aand, it's that error again:</p>
<pre><code class="language-text">LLM error: {
"error": {
"code": 404,
"message": "models/gemini-2.5-flash-preview-09-2025 is not found for API version v1beta, or is not supported for generateContent. Call ListModels to see the list of available models and their supported methods.",
"status": "NOT_FOUND"
}
}
</code></pre>
<p>Turns out changing the model doesn't stick unless you restart the gateway.
Entertainingly I asked it to restart the gateway in the TUI. It killed the gateway and
then immediately went unresponsive, not having a gateway to work with.</p>
<p>Restarting it... <code>openclaw gateway</code>: Conveniently that opens it in the foreground, it
looks great in tmux.</p>
<h2 id="set-up-the-tone-and-such">Set up the tone and such</h2>
<p>When the messaging finally works on Telegram it'll ask you for an identity and tone.
It's kind of fun.</p>
<h1 id="shutdown-procedure">Shutdown Procedure</h1>
<p>Don't wanna leave this thing running until it's secured.</p>
<pre><code class="language-bash"># Stop the gateway
openclaw gateway stop

# Verify nothing is still running
ps aux | grep openclaw
pkill -f openclaw-gateway
</code></pre>
<h1 id="whats-next">What's next</h1>
<p>I wanted to set this up with Linear, but honestly I don't love it runing flat on my
Macbook. I'm gonna <a href="/openclaw-k8s.html">put it in K8s</a> and producitonize this now
that I've got the MVP figured out.</p>


<p>Linear's official MCP server launched in May 2025. It lets the robots lean in on the
project management work. I want to get some ideation-bots going.</p>
<h2 id="supported-commands">Supported Commands</h2>
<table>
<thead>
<tr>
<th>Category</th>
<th>Commands</th>
</tr>
</thead>
<tbody>
<tr>
<td>Issues</td>
<td>list, get, create/update, status, statuses</td>
</tr>
<tr>
<td>Projects</td>
<td>list, get, create/update, labels</td>
</tr>
<tr>
<td>Documents</td>
<td>list, get, create, update, search</td>
</tr>
<tr>
<td>Comments</td>
<td>list, create/update, delete</td>
</tr>
<tr>
<td>Attachments</td>
<td>get, create, delete, extract images</td>
</tr>
<tr>
<td>Milestones</td>
<td>list, get, create/update</td>
</tr>
<tr>
<td>Cycles</td>
<td>list</td>
</tr>
<tr>
<td>Teams</td>
<td>list, get</td>
</tr>
<tr>
<td>Users</td>
<td>list, get</td>
</tr>
<tr>
<td>Labels</td>
<td>list issue labels, create issue label</td>
</tr>
</tbody>
</table>
<h2 id="setup">Setup</h2>
<p>Linear hosts the MCP server at <code>https://mcp.linear.app/mcp</code>. Use that endpoint, not the
old SSE one which is deprecated.</p>
<h3 id="claude-code">Claude Code</h3>
<pre><code class="language-bash">claude mcp add --transport http linear-server https://mcp.linear.app/mcp
</code></pre>
<p>Restart and authenticate via OAuth.</p>
<hr>
<h1 id="idea-nightly-ideation-engine">Idea: Nightly Ideation Engine</h1>
<p>Instead of using Linear as a human-operated task tracker, an AI agent can read youri
codebase, compare it against goals, and file suggestions directly.</p>
<p>What I'm hoping to build is a system where a scheduled agent runs nightly. It reads
blog posts, checks page speed, reviews SEO metadata, and compares everything against a
set of high-level goals:</p>
<ul>
<li>100% accurate content</li>
<li>Fast loading</li>
<li>Low cost</li>
<li>Looks good</li>
<li>Good SEO</li>
<li>Interesting tutorials about emerging tooling</li>
</ul>
<p>It should create Linear issues for anything worth improving, labeled "Suggestion" and
drop them into the Backlog.</p>
<h3 id="the-pipeline">The pipeline</h3>
<p>Linear's built-in statuses act as gates between human and agent
work.</p>
<pre class="mermaid">
stateDiagram-v2
    [*] --> Backlog: AI files suggestion
    Backlog --> Todo: Human promotes
    Backlog --> Canceled: Human rejects
    Todo --> InProgress: Agent picks up
    InProgress --> InReview: Agent finishes
    InReview --> Done: Human approves
    InReview --> InProgress: Human pushes back
</pre>
<p>Each step has a clear owner:</p>
<table>
<thead>
<tr>
<th>Status</th>
<th>Owner</th>
<th>What happens</th>
</tr>
</thead>
<tbody>
<tr>
<td>Backlog</td>
<td>AI</td>
<td>Nightly agent creates suggestion issues</td>
</tr>
<tr>
<td>Todo</td>
<td>Human</td>
<td>You move the good ones forward</td>
</tr>
<tr>
<td>In Progress</td>
<td>Agent</td>
<td>A working agent picks it up</td>
</tr>
<tr>
<td>In Review</td>
<td>Agent</td>
<td>Agent finishes, leaves a comment</td>
</tr>
<tr>
<td>Done</td>
<td>Human</td>
<td>You verify and close</td>
</tr>
<tr>
<td>Canceled</td>
<td>Human</td>
<td>Bad idea, toss it</td>
</tr>
</tbody>
</table>
<h3 id="agent-notes-via-comments">Agent notes via comments</h3>
<p>When agents work on an issue, they leave comments. This beats
internal context files because comments are visible, searchable,
and survive across sessions. If one agent starts a task and another
finishes it, the full history is right there on the issue.</p>
<p>I might also run multiple planning agents that each comment on a
ticket before I review it. A fact-checker, a SEO reviewer, and a
style auditor could all weigh in on the same Backlog issue. By the
time I look at it, there's already a thread of perspectives.</p>
<pre><code class="language-bash"># An agent checking for work:
# list_issues with label="Suggestion", state="Todo"

# An agent leaving notes:
# save_comment with issueId and markdown body
</code></pre>
<h3 id="filtering">Filtering</h3>
<p>The nightly agent queries existing Backlog suggestions before
creating new ones, avoiding duplicates. Working agents filter
by label and status to find their next task:</p>
<pre><code>list_issues → label: "Suggestion", state: "Todo"
</code></pre>
<h2 id="defining-state-as-prompts">Defining State as Prompts</h2>
<p>Linear's community Terraform provider exists but only covers teams,
labels, workflow states, and templates. No custom views. The GraphQL
API has a <code>customViewCreate</code> mutation, but nobody's wrapped it in
IaC tooling yet.</p>
<p>So instead of declaring views in HCL, I described mine as a prompt
and had Claude set it up via <a href="/playwright-mcp.html">Playwright MCP</a>:</p>
<blockquote>
<p>Create a custom view called "Suggestion Backlog" on the Pericak
team. Filter to issues where label includes "Suggestion" and
status is "Backlog". Description: "AI-generated improvement
suggestions awaiting human review".</p>
</blockquote>
<p>Claude navigated to Linear in Chromium, clicked "Add new view",
filled in the name, added both filters, and hit Save. The view
now lives as a tab on the team page.</p>
<p>This is nondeterministic state management. There's no lock file,
no plan/apply, no drift detection. But it works, and the prompt
is the documentation. If the view ever gets deleted, re-run the
prompt.</p>
<h3 id="a-linear-admin-agent">A Linear-Admin agent</h3>
<p>A "Linear-Admin" agent could own all workspace
configuration: views, labels, workflow states, notification
settings. You'd feed it a doc describing your desired workspace
setup, and it would use Playwright to converge Linear's UI to
match. Like Terraform, but with a browser instead of an API.</p>
<p>The tradeoffs are real. Playwright clicks are slow, brittle if
Linear redesigns their UI, and hard to make idempotent. But for
a personal workspace where the alternative is "click around in
the UI manually," the prompt-as-IaC approach is surprisingly
practical.</p>
<p>Still faster than a person.</p>


Kyle Pericak

Writing My Own OpenClaw Skill for Linear

OpenClaw MVP

Linear MCP: Planning with Robots