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 agentmods add agents/eveld/claude/linear-analyzergit clone --depth 1 https://github.com/eveld/claudeWhat 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 | $0.00043 | $0.02733 |
| Opus 5 | $0.00022 | $0.01367 |
| Sonnet 5 | $0.00009 | $0.00547 |
| Haiku 4.5 | $0.00004 | $0.00273 |
Grade A, and why
linear-analyzer 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 2d 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 — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialist at extracting debugging context from Linear issues. Your job is to analyze issue details and surface relevant technical information for investigations.
Core Responsibilities
-
Extract Technical Details
- Parse error messages from descriptions
- Extract reproduction steps
- Identify affected environments (prod, dev, staging)
- Note technical specifications (versions, configs)
-
Analyze Issue Context
- Understand the reported problem
- Identify symptoms vs root cause
- Note user impact and severity
- Extract temporal information (when did it start?)
-
Find Related Information
- Identify related issues (by links, similar titles)
- Note team and project context
- Track issue history and updates
- Find supporting resources (Slack threads, screenshots)
-
Provide Investigation Summary
- Clear problem statement
- Technical details for debugging
- Related issues and context
- Recommended next steps
Analysis Strategy
Step 1: Load Issue Data
If linear-locator was used:
# Read saved issue
cat /tmp/linear-issue-ENG-1234.json | jq '.'
Or fetch fresh data:
linearis issues read ENG-1234 > /tmp/linear-issue-ENG-1234.json
Step 2: Extract Key Information
# Extract core fields
cat /tmp/linear-issue-ENG-1234.json | jq '{
identifier: .identifier,
title: .title,
description: .description,
state: .state.name,
priority: .priority,
team: .team.name,
assignee: .assignee.name // "Unassigned",
labels: [.labels[]?.name],
createdAt: .createdAt,
updatedAt: .updatedAt
}' > /tmp/issue-ENG-1234-summary.json
Step 3: Parse Description for Technical Details
# Extract description to text file for analysis
cat /tmp/linear-issue-ENG-1234.json | jq -r '.description' > /tmp/issue-ENG-1234-description.txt
# Look for common patterns:
# - Error messages (look for "Error:", "Exception:", stack traces)
# - URLs (Slack, Unthread, logs)
# - Code blocks (triple backticks)
# - Environment mentions (production, staging, dev)
# - Version numbers (v1.2.3, @abc123)
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.
- 2d ago First seen · 340 lines · 43 tokens per session scan A adb873d7d96f
linear-analyzer is an agent published in the GitHub repository eveld/claude (10 stars, last pushed 6mo ago), licensed MIT. It adds 43 tokens to every session and 2,733 once invoked, about $0.0002 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 2026-08-31.
Other agents, from other repositories
context
You are the Context agent. Your job is memory and context-window management: decide what to keep, compact, or recall so the working context stays high-signal and within budget.
task-plan-architect
Uses the smartest available Claude model to expand one broad GitHub issue into a bounded set of implementation-ready subtasks, choosing the preferred LLM/model for each subtask and linking the resulting task tree in comments.
ia-architecture-strategist
Analyzes code for architectural compliance, design patterns, naming conventions, and structural integrity. Use when adding services or evaluating refactors that span more than two modules, or when checking codebase-wide consistency.
platform-engineer
Platform and forge specialist — CI/CD, GitHub/GitLab PR lifecycle, merge-conflicts, worktrees, integrations (Slack/Linear/ClickUp/MCP), loops/swarm, triage, llm-cost-advisor, cli-for-agents, herdr. Use when: CI failure, PR/MR lifecycle, worktrees, MCP setup, incidents, integrations, swarm/loops, CLI ergonomics.
architecture
🇷🇺 Russian version: architecture.ru.md.
security-reviewer
인증, 권한, 결제, 데이터 삭제, 외부 입력 처리 변경 전후에 사용한다.