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 7df-lab/devo --skill lineargit clone --depth 1 https://github.com/7df-lab/devoWrote 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/7df-lab/devo/linear)<a href="https://agentmods.dev/skills/7df-lab/devo/linear"><img src="https://agentmods.dev/badge/skills/7df-lab/devo/linear/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/7df-lab/devo/linear"><img src="https://agentmods.dev/badge/skills/7df-lab/devo/linear.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00034 | $0.00381 |
| Opus 5.5 | $0.00014 | $0.00152 |
| Sonnet 5.5 | $0.00007 | $0.00076 |
| Haiku 4.5 | $0.00003 | $0.00038 |
Grade A, and why
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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- linear — 100% identical, 0 lines differ
What it actually says
Linear
Talk to Linear through its official hosted MCP server from the Python kernel.
Setup
Connect via /login → Services tab → Linear (OAuth in the browser).
/mcp login linear does the same. Once connected, this skill is enabled
automatically. If a call raises NotEnabled, the user isn't logged in — walk
them through /login; don't ask them to set environment variables.
Usage
The tool set is defined by the server, not by this skill, so discover before you call — don't assume tool names or argument names:
import linear
# 1. Discover available tools
for tool in await linear.list_tools():
print(tool["name"], "-", tool["description"])
# 2. Inspect a specific tool's arguments (rendered from its JSON Schema)
help(linear.list_issues)
# 3. Call it; keyword args must match the tool's input schema
result = await linear.list_issues(team="Engineering")
print(result)
Notes:
- Every tool is an
asyncmethod — alwaysawait. - Results are already-parsed Python (a
dictfor structured output, otherwise a string). No need tojson.loadsthem. - For tools whose names aren't valid Python identifiers, use the escape hatch:
await linear.call_tool("tool-name", {"arg": "value"}). - Run
list_tools()before relying onhelp()or assuming a tool exists — it populates the schemashelp()shows, and the server is the source of truth for tool names and arguments.
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.
- 10d ago First seen · 46 lines · 34 tokens per session scan A fc54d01c0d01
linear is a skill published in the GitHub repository 7df-lab/devo (321 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 381 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-06.
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gh-file-issue
Use when filing a new Codewhale GitHub issue: turn a bug or idea into a well-formed, actionable issue with repro, acceptance criteria, labels, and milestone.
gh-treasure-hunt
Hunt the issue/PR queue for highest value-over-risk wins: clean focused community PRs, already-implemented issues to close, safe quick-fixes.
gh-assign-issues
Use to assign GitHub issues to a milestone and/or owners in bulk, verifying each.