NanoClaw is an AI assistant that runs agents inside separate Linux containers, isolating their files and execution environments. People use it to connect agents to messaging services and run assistants with memory and scheduled jobs. The catalogue contains skills and instructions for extending or operating NanoClaw.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nanocoai/nanoclaw --skill add-lineargit clone --depth 1 https://github.com/nanocoai/nanoclawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nanocoai/nanoclaw/add-linear)<a href="https://agentmods.dev/skills/nanocoai/nanoclaw/add-linear"><img src="https://agentmods.dev/badge/skills/nanocoai/nanoclaw/add-linear.svg" alt="Measured on agentmods" height="20"></a>- Snyk fail
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.02406 |
| Opus 5 | $0.00009 | $0.01203 |
| Sonnet 5 | $0.00003 | $0.00481 |
| Haiku 4.5 | $0.00002 | $0.00241 |
Grade A, and why
add-linear scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Linear Channel
Adds Linear support via the Chat SDK bridge. The agent participates in issue
comment threads. Every comment on a Linear issue triggers the agent — no
@-mention needed. NanoClaw doesn't ship channels in trunk — this skill copies the
Linear adapter in from the channels branch.
The mechanical steps under Apply carry nc: directive fences: an agent reads
the prose and applies them, and a parser can apply them deterministically from
the same document. Every directive is idempotent, so the whole skill is safe to
re-run; anything a parser can't apply falls back to the prose beside it.
Prerequisites
Recommended: Create a Linear OAuth application so the agent posts as an app identity, not as you. This prevents the adapter from filtering your own comments as self-messages.
- Go to Linear Settings > API > OAuth Applications
- Create an app (e.g. "NanoClaw Bot")
- Developer URL: your repo URL (e.g.
https://github.com/your-org/nanoclaw) - Callback URL:
http://localhost
- Developer URL: your repo URL (e.g.
- After creating, click the app and enable Client credentials under grant types
- Copy the Client ID and Client Secret
Alternative: Use a Personal API Key (LINEAR_API_KEY) for simpler setup. The agent will post as you, and your own comments will be filtered (other team members' comments still work).
Apply
Linear OAuth apps post and read comments under an app identity that can't be @-mentioned; the adapter's declared channel defaults therefore respond to plain comments rather than mention-only, and the wiring below sets that same pattern mode explicitly.
1. Copy the adapter and its registration test
Fetch the channels branch and copy the Linear adapter and its registration
test into src/channels/ (overwrite — the branch is canonical):
src/channels/linear.ts
src/channels/linear-registration.test.ts
2. Register the adapter
Append the self-registration import to the channel barrel (skipped if the line is already present). This one line is the skill's only reach-in into the channel registry:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 209 lines · 17 tokens per session scan A 53fac0a49114
add-linear is a skill published in the GitHub repository nanocoai/nanoclaw (30,711 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 2,406 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
linear
Manage Linear issues, projects, and teams via the GraphQL API. Create, update, search, and organize issues. Uses API key auth (no OAuth needed). All operations via curl — no dependencies.
notion
Notion API for creating and managing pages, databases, and blocks via curl. Search, create, update, and query Notion workspaces directly from the terminal.
pm-brainstorm
A guided brainstorming workflow for exploring a new product idea before detailed research or requirements work. It collects basic information about the product, users, and problem through staged questions.
start-super-pm
Use when starting any product management task - automatically detects task type and invokes appropriate PM skill using intelligent routing.
pm-priority
A Chinese-language procedure for ranking several product requests when time or resources are limited. It supports scoring or grouping methods such as RICE, KANO, and MoSCoW to decide what should be done first.
pm-change
A requirements-change workflow for recording a proposed change, assessing its effects, and deciding whether to approve it.