Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/hybridlabor-api/bdb-dev-optimized-agent-skillsnpx agentmods add skills/hybridlabor-api/bdb-dev-optimized-agent-skills/linear-claude-skillWrote 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/hybridlabor-api/bdb-dev-optimized-agent-skills/linear-claude-skill)<a href="https://agentmods.dev/skills/hybridlabor-api/bdb-dev-optimized-agent-skills/linear-claude-skill"><img src="https://agentmods.dev/badge/skills/hybridlabor-api/bdb-dev-optimized-agent-skills/linear-claude-skill/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/hybridlabor-api/bdb-dev-optimized-agent-skills/linear-claude-skill"><img src="https://agentmods.dev/badge/skills/hybridlabor-api/bdb-dev-optimized-agent-skills/linear-claude-skill.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 | $0.00015 | $0.03520 |
| Opus 5 | $0.00008 | $0.01760 |
| Sonnet 5 | $0.00003 | $0.00704 |
| Haiku 4.5 | $0.00002 | $0.00352 |
Grade A, and why
linear-claude-skill 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 7d 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.
This is a copy
86% identical to linear-claude-skill — 30 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 525 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use This Skill
Manage Linear issues, projects, and teams
Use this skill when working with manage linear issues, projects, and teams.
Linear
Tools and workflows for managing issues, projects, and teams in Linear.
⚠️ Tool Availability (READ FIRST)
This skill supports multiple tool backends. Use whichever is available:
- MCP Tools (mcp__linear) - Use if available in your tool set
- Linear CLI (
linearcommand) - Always available via Bash - Helper Scripts - For complex operations
If MCP tools are NOT available, use the Linear CLI via Bash:
# View an issue
linear issues view ENG-123
# Create an issue
linear issues create --title "Issue title" --description "Description"
# Update issue status (get state IDs first)
linear issues update ENG-123 -s "STATE_ID"
# Add a comment
linear issues comment add ENG-123 -m "Comment text"
# List issues
linear issues list
Do NOT report "MCP tools not available" as a blocker - use CLI instead.
🔐 Security: Varlock Integration
CRITICAL: Never expose API keys in terminal output or Claude's context.
Safe Commands (Always Use)
# Validate LINEAR_API_KEY is set (masked output)
varlock load 2>&1 | grep LINEAR
# Run commands with secrets injected
varlock run -- npx tsx scripts/query.ts "query { viewer { name } }"
# Check schema (safe - no values)
cat .env.schema | grep LINEAR
Unsafe Commands (NEVER Use)
# ❌ NEVER - exposes key to Claude's context
linear config show
echo $LINEAR_API_KEY
printenv | grep LINEAR
cat .env
Setup for New Projects
-
Create
.env.schemawith@sensitiveannotation:# @type=string(startsWith=lin_api_) @required @sensitive LINEAR_API_KEY= -
Add
LINEAR_API_KEYto.env(never commit this file) -
Configure MCP to use environment variable:
{ "mcpServers": { "linear": { "env": { "LINEAR_API_KEY": "${LINEAR_API_KEY}" } } } }
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.
- 7d ago First seen · 525 lines · 15 tokens per session scan A c89db538b752
linear-claude-skill is a skill published in the GitHub repository hybridlabor-api/bdb-dev-optimized-agent-skills (6 stars, last pushed 5d ago), licensed Apache-2.0. It adds 15 tokens to every session and 3,520 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to linear-claude-skill, differing in 30 lines, and is treated as a copy.
Other skills, from other repositories
babysit-babysitter-issues
This skill should be used when the user asks to "babysit issues", "work on assigned issues", "check a5c-agent issues", "process babysitter issues", or wants to find and work on open GitHub issues assigned to a5c-agent in the babysitter repo.
cog-meeting-processing
Process meeting recordings and transcripts into decisions, action items, and team dynamics.
cog-team-intelligence
Cross-reference GitHub, Linear, Slack, and PostHog with bidirectional sync for team briefs.
agentrq
Lead multiple agents to accomplish big tasks with specialized workspaces/agents by assigning tasks to specialized agents.
agentrq
Execute tasks assigned by humans or supervisor agents within a specific AgentRQ workspace. Use when you receive channel messages or need to report progress on tasks.
swarm-orchestration
Multi-agent swarm formation and coordinated execution with topology-aware agent deployment, consensus protocols, and anti-drift enforcement.