agentleFS
jerseycheesejerseycheese/agent-skills/skills/analyze-issue/SKILL.md

analyze-issue

Skill4 starsChanged 16 days agoMITView on GitHub

Turns one GitHub issue into an implementation-ready tech spec: existing patterns, scope boundaries, a plan, and MVP tests. Use before starting work on an issue. Trigger on: "analyze issue #X", "spec out #123", "break down this issue", "plan issue #N".

4 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

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

    line 25
  2. Distinctive:

    Before planning anything, find what already exists in the codebase.

    line 29
  3. Distinctive:

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

    line 54
  4. Distinctive:

    If the issue has ambiguous requirements, call them out explicitly rather than assuming.

    line 124

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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 reuse
- [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

List only the essential tests: one per distinct behavior the change adds or alters, usually one or two, and none for a copy, config, or one-line layout fix. A bug fix always gets the regression test that would have caught the bug. Each test must fail if the behavior it covers is reverted and must guard something a user or caller would notice breaking. Plan per behavior, not per criterion. Then cut: for each test, ask "if I delete this, what bug could ship?" No concrete answer means drop it. No "renders without crashing."

1. [What you're testing] — catches [the breakage a user or caller would notice]

Not testing (save for later or explicitly out of scope):
- [edge case not in acceptance criteria, unless the bug itself is that edge case]
- [exhaustive input validation]

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

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

## Success criteria
- [ ] [criterion pulled directly from issue]
- [ ] [criterion]
- [ ] Each planned test guards one essential behavior (or the bug's regression) without rigging
- [ ] No duplicate functionality introduced

## Risks
- [Risk]: [Mitigation]

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

6. Flag anything unclear

If the issue has ambiguous requirements, call them out explicitly rather than assuming. Ask the user to clarify before they start implementing — it's cheaper to resolve ambiguity now than mid-implementation.

7. Handing off to implementation

Once the spec is confirmed and ambiguity is resolved, suggest driving the build with /goal instead of manual turn-by-turn iteration — the success criteria above become the completion condition, e.g. /goal "issue #N: all success-criteria checkboxes met, tests green, PR open". A separate evaluator decides when it's actually done.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.