analyze-github-issue

analyze-github-issue is a skill for Claude Code from TNT-Likely/honeycomb. It costs 166 tokens per session (1,195 once invoked), scanned A, original, MIT.

A workflow for deeply assessing one GitHub issue, including whether it is a real bug or a reasonable feature request.

In plain words
What is it for?
Use it to investigate an issue's cause, impact, reproducibility, priority, feasibility, and whether it should be addressed.
Why use it?
It checks the issue against the code and available evidence instead of relying only on the issue description.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the github-issue-triage plugin — 2 skills shipped together

Good fit Use it to investigate an issue's cause, impact, reproducibility, priority, feasibility, and whether it should be addressed.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tnt-likely/honeycomb/analyze-issue
Install

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.

Any agent
npx skills add TNT-Likely/honeycomb --skill analyze-issue
Clone the repo
git clone --depth 1 https://github.com/TNT-Likely/honeycomb

Made for: Claude Code.

Or install github-issue-triage, the plugin that ships this one along with the rest of its 2 skills.

Wrote 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.

agentmods badge for analyze-github-issue

README.md
[![agentmods](https://agentmods.dev/badge/skills/tnt-likely/honeycomb/analyze-issue.svg)](https://agentmods.dev/skills/tnt-likely/honeycomb/analyze-issue)
Your own site
<a href="https://agentmods.dev/skills/tnt-likely/honeycomb/analyze-issue"><img src="https://agentmods.dev/badge/skills/tnt-likely/honeycomb/analyze-issue.svg" alt="Measured on agentmods" height="20"></a>
Per session 166 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,195 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00166 $0.01195
Opus 5 $0.00083 $0.00598
Sonnet 5 $0.00033 $0.00239
Haiku 4.5 $0.00017 $0.00120

Measured 7d ago against content hash 4c14a127ac63, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

analyze-github-issue 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.

plugins/github-issue-triage/skills/analyze-issue/SKILL.md · 73 lines

What it actually says

单 Issue 深度分析技能

做什么

一条 GitHub issue 做透彻分析,回答四个问题:

  1. 是不是真问题? —— bug 真实存在 / 使用误解 / 已被修复 / 无法复现
  2. 要不要修 / 要不要做?
  3. 优先级多高? 4.(功能请求)合理吗?契合产品吗?

github-issue-triage 技能互补:triage 是宏观全量排序,本技能是单点深挖。常见组合:triage 选出可疑/高价值的,再用本技能逐个做透。

流程

1. 拉取 issue 全文

env -u HTTPS_PROXY -u HTTP_PROXY -u https_proxy -u http_proxy -u ALL_PROXY -u all_proxy \
gh issue view N --repo OWNER/REPO \
  --json number,title,body,labels,comments,reactionGroups,state,author

读完整 body 加所有评论 —— 评论里常有复现补充、维护者回应、重复线索、版本信息。

2. 判类型

bug / 功能请求 / question(使用问题) / 重复 / 信息不足。先定性,再走对应分支。

3a. 如果是 Bug —— 必须用代码验证,别只信描述

  • 在 codebase 定位相关代码,确认 bug 真实存在(找到会出错的那几行)
  • 讲清:根因(哪行、为什么错)、影响面(谁会中招、多频繁)、能否复现(给最小路径)、是否已被其它改动修掉
  • 区分:真 bug / 配置或使用问题 / 环境特定 / 描述不实
  • 危害分级:数据正确性错误、崩溃 最高;偶发体验问题低
  • 是否需要修的明确结论 + 修复思路 + 工作量(S/M/L)

实战参照:有的 issue 报"AI 解析失败",定位到 amount as num? 强转字符串直接崩 —— 真 bug,可定位可修;有的报"自动记账没反应"实为通知权限没开 —— 使用问题,引导即可,不动代码。结论必须落到代码或事实,不能凭描述拍脑袋。

3b. 如果是功能请求 —— 评估合理性,而非照单全收

  • 契合度:符合产品定位与现有信息架构吗?会不会把 app 撑成四不像?
  • 普遍 vs 小众:看呼声(👍/💬)+ 是不是只有提出者一个人的特殊流程
  • 已有替代:现有功能能否绕过 / 组合实现?
  • 可行性 / 成本:技术工作量?有无平台 / 合规限制(如某些权限上架受限)
  • 拆分:大需求拆成"先做最痛的小核心"
  • 结论:建议做 / 可做但不急 / 不建议做(给理由)/ 需求方补充信息

4. 输出结构

## #N <标题>
- 类型:bug / feature / question / …
- 结论:<真 bug 建议修 / 合理,P1 建议做 / 使用问题,引导即可 / 不建议做:…>
- 依据:<代码定位 file:line / 呼声 / 契合度分析>
- 优先级:🔴P0 / 🟡P1 / 🟢P2 / ⚪P3 + 一句理由
- 工作量:S / M / L
- 下一步:<修复思路 / 拆分方案 / 要追问的信息>

可直接作为 issue 评论草稿交给用户,但不自动发评论、不改 issue 状态

红线

  • bug 结论必须有代码依据 —— 不能只凭 issue 描述断言"是 bug"
  • 功能请求诚实评估,该说"不建议做"就说清理由 —— 不当老好人照单全收
  • 只读:不自动评论 / 关闭 / 改 label,产出供人决策
Changes

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.

  1. 7d ago First seen · 73 lines · 166 tokens per session scan A 4c14a127ac63

Subscribe to this mod's changes

analyze-github-issue is a skill published in the GitHub repository TNT-Likely/honeycomb (2 stars, last pushed 7d ago), licensed MIT. It adds 166 tokens to every session and 1,195 once invoked, about $0.0008 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.

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