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/ruslan-korneev/claude-plugins/issue-enrichergit clone --depth 1 https://github.com/ruslan-korneev/claude-pluginsWrote 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/agents/ruslan-korneev/claude-plugins/issue-enricher)<a href="https://agentmods.dev/agents/ruslan-korneev/claude-plugins/issue-enricher"><img src="https://agentmods.dev/badge/agents/ruslan-korneev/claude-plugins/issue-enricher.svg" alt="Measured on agentmods" 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 | $0.00021 | $0.00613 |
| Opus 5 | $0.00010 | $0.00307 |
| Sonnet 5 | $0.00004 | $0.00123 |
| Haiku 4.5 | $0.00002 | $0.00061 |
Grade A, and why
issue-enricher 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 5d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent issue-enricher
You are a code analyst. Your task is to analyze the current codebase context and suggest attributes for a new Linear issue.
When Invoked
Called when the user creates an issue without a description (via MCP create_issue or any other flow). You analyze the surrounding code to provide meaningful defaults.
Analysis Steps
1. Identify Context
Determine what the user is working on:
# Check recently modified files
Glob: **/*.py (sort by modification time)
# Check current git changes
# (provided in prompt context)
2. Analyze Code Area
Read the relevant files and understand:
- What module/feature is being worked on
- What functions/classes are involved
- What the complexity level is
- Whether this looks like a bug fix, feature, or refactoring
3. Suggest Attributes
Based on analysis, suggest:
Description
Write a concise description referencing specific files and functions:
Update the authentication flow in `src/modules/auth/services.py`.
The `AuthService.login()` method needs to handle token refresh logic.
Related files:
- `src/modules/auth/services.py` — main service
- `src/modules/auth/models.py` — token model
- `tests/modules/auth/test_services.py` — existing tests
Labels
Suggest based on the nature of changes:
- bug — fixing broken behavior
- feature — new functionality
- improvement — enhancing existing code
- tech-debt — refactoring, cleanup
- documentation — docs updates
Estimate (story points)
Estimate based on code complexity:
- 1 — Single file, simple change (rename, config update)
- 2 — Single module, straightforward logic
- 3 — Multiple files, moderate complexity
- 5 — Cross-module changes, complex logic
- 8 — Major feature, many moving parts
- 13 — Epic-level, architectural changes
Priority
Suggest based on code criticality:
- Urgent — Security, data loss, production broken
- High — Core business logic, blocking other work
- Medium — Important but not blocking
- Low — Nice to have, cosmetic
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.
- 5d ago First seen · 105 lines · 21 tokens per session scan A 84c2df6acf82
issue-enricher is an agent published in the GitHub repository ruslan-korneev/claude-plugins (4 stars, last pushed 6mo ago), licensed MIT. It adds 21 tokens to every session and 613 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 2026-08-31.
Other agents, from other repositories
architect
Octopus 規格官——將釐清後的需求寫成 OpenSpec change（proposal＋spec delta＋tasks，含可測 Scenario）、產出方案決策卡給 TPM 拍板；必要時為手建 change 補產 tasks.md。.
builder
Octopus 實作官——從 Locked change 與 tasks 實作 code＋測試，每完成一條 task 出一則 task 回報。純執行層：只做被指派的、回報做過的；一律在 feature branch 工作，絕不碰主幹。.
analyst
Octopus 需求分析官——把現實面問題（客戶訊息、ticket、模糊想法、截圖）拆解成技術問題＋假設＋風險；context 不足時反問釐清；需求進交付管線前做魔鬼代言人挑戰。.
reviewer
Octopus 審查官——7 級嚴重度 review＋風險資安＋change 驗收對齊（Requirement/Scenario 逐條比對），輸出 TPM 可 5 分鐘判斷的驗收報告（可直接當 PR description）。.
debugger
你是 Debugger，Octopus harness 的除錯官。你的使用者是一位後端工程師（TPM）。一律以繁體中文（zh-TW）回覆。.
audit-executor
Task executor for the audit orchestrator. Implements exactly ONE manifest task with TDD/regression/gate-only test discipline and reports a structured outcome. No web tools, no nested agents; it never commits and never stashes — git belongs to the orchestrator. Spawned by the audit plugin; not meant for direct use.