analyze-issue

analyze-issue is a skill for Claude Code from jerseycheese/agent-skills. It costs 66 tokens per session (975 once invoked), scanned A, original, MIT.

A workflow for turning one GitHub issue into a technical specification and implementation plan.

In plain words
What is it for?
Use it before implementing an issue to understand its requirements, constraints, reusable code, acceptance criteria, and minimum test coverage.
Why use it?
It gathers the issue's description and comments, checks existing code patterns, and defines scope and initial tests before coding begins.

Skill for Claude Code

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

Part of the workflow-skills plugin — 31 skills shipped together

Good fit Use it before implementing an issue to understand its requirements, constraints, reusable code, acceptance criteria, and minimum test coverage.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jerseycheese/agent-skills/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 jerseycheese/agent-skills --skill analyze-issue
Clone the repo
git clone --depth 1 https://github.com/jerseycheese/agent-skills

Made for: Claude Code.

Or install workflow-skills, the plugin that ships this one along with the rest of its 31 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-issue

README.md
[![agentmods](https://agentmods.dev/badge/skills/jerseycheese/agent-skills/analyze-issue/github.svg)](https://agentmods.dev/skills/jerseycheese/agent-skills/analyze-issue)
Your own site
<a href="https://agentmods.dev/skills/jerseycheese/agent-skills/analyze-issue"><img src="https://agentmods.dev/badge/skills/jerseycheese/agent-skills/analyze-issue/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.

agentmods 80×15 button for analyze-issue

Your own site · 80×15
<a href="https://agentmods.dev/skills/jerseycheese/agent-skills/analyze-issue"><img src="https://agentmods.dev/badge/skills/jerseycheese/agent-skills/analyze-issue.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 975 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 $0.00066 $0.00975
Opus 5 $0.00033 $0.00487
Sonnet 5 $0.00013 $0.00195
Haiku 4.5 $0.00007 $0.00097

Measured 8d ago against content hash eb3c96248f54, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

analyze-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 8d 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.

skills/analyze-issue/SKILL.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Issue Analysis

1. Get the issue number

The user will provide an issue number. If they described an issue by name instead, find it:

gh issue list --search "[keywords]" --json number,title | head -5

2. Fetch full issue context

gh issue view [NUMBER] --json number,title,body,labels,comments,createdAt,milestone,assignees

Read every comment — they often contain updated requirements, implementation constraints, or scope decisions that aren't in the original body.

3. Discover existing patterns

Before planning anything, find what already exists in the codebase. Don't assume new code is needed.

# Find related components by keyword
grep -r "[relevant term]" src/ --include="*.ts" --include="*.tsx" -l | head -10

# Check for similar features
find src/ -name "*.ts" -o -name "*.tsx" | xargs grep -l "[related concept]" | head -5

Look for:

  • Existing components that could be extended
  • Utilities that could be reused
  • Types or interfaces to build on
  • Patterns established by similar features

4. Analyze requirements

From the issue body and comments, extract:

  • The core problem being solved
  • Explicit acceptance criteria (look for checklists or "should" statements)
  • What the user asked for vs what they actually need (they may differ)
  • Any constraints mentioned (performance, backwards compatibility, etc.)
  • What's explicitly out of scope

If acceptance criteria are vague, infer concrete ones from the description.

5. Produce the technical specification

# Technical Spec: Issue #[NUMBER] — [title]

## Summary
[2-3 sentences: what problem this solves and why it matters]

Labels: [labels]
Milestone: [milestone or "none"]
Priority: [High/Medium/Low based on issue content]

## Scope

In scope:
- [specific item]
- [specific item]

Out of scope:
- [explicitly excluded item]
- [adjacent work that might seem related but isn't]

## Technical approach
[Concrete approach that leans on existing patterns. Reference specific files or components
found during discovery. Explain key decisions — why this approach over alternatives.]

## Existing code to leverage
- [file/component]: [how it applies]
- [utility]: [what it provides]
- [type/interface]: [what to extend]

## Implementation plan
1. [Step — specific enough to act on]
2. [Step]
3. [Step]
4. [Step, if needed]

## MVP test plan

Write 3-5 tests that map directly to acceptance criteria. No "renders without crashing."

1. [What you're testing] — validates [acceptance criterion]
2. [What you're testing] — validates [acceptance criterion]
3. [What you're testing] — validates [acceptance criterion]

Not testing (save for later or explicitly out of scope):
- [edge case not in acceptance criteria]
- [exhaustive input validation]

## Files to modify
- [path]: [changes]

## Files to create
- [path]: [purpose]

## Success criteria
- [ ] [criterion pulled directly from issue]
- [ ] [criterion]
- [ ] Tests cover acceptance criteria without rigging
- [ ] No duplicate functionality introduced

## Risks
- [Risk]: [Mitigation]

## Estimated effort
[Small / Medium / Large — with a one-sentence rationale]

Read the full file on GitHub · 128 lines

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. 8d ago First seen · 128 lines · 66 tokens per session scan A eb3c96248f54

Subscribe to this mod's changes

analyze-issue is a skill published in the GitHub repository jerseycheese/agent-skills (1 stars, last pushed 7d ago), licensed MIT. It adds 66 tokens to every session and 975 once invoked, about $0.0003 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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