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fatih-developerfatih-developer/fth-skills/skills/project-analyzer/SKILL.md

project-analyzer

Skill7 starsChanged 42 days agoMITView on GitHub

Deeply analyze an existing software project and write 3-4 evidence-based reports to docs/analyze/ (overview, architecture and code quality, risks and recommendations, plus an API endpoint list when an API exists). Use when the user asks to analyze the project, review the codebase, produce an architecture report, assess technical debt, or evaluate a repository; also for Turkish requests such as 'projeyi analiz et', 'kod analizi yap', 'mimari rapor', 'teknik borç'. Reports are written in the user's language.

8 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

6 rules. 0 are common, 5 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 5 edited versions we read keep these.

  • 5

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Report language: Write the reports in the language the user is using.

    line 18
  2. Distinctive:

    Core rule: Do not perform a superficial review.

    line 27
  3. Distinctive:

    Test presence: does it exist, is it prevalent?

    line 43
  4. Very specific:

    Create the docs/analyze/ folder and save the 3 (or 4) reports.

    line 68
  5. Distinctive:

    If no project directory is provided and the user is just asking a general question.

    line 390
  6. Distinctive:

    If there is no code to analyze (empty project, only configuration files).

    line 391

“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “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

Project Analyzer Skill

Outputs:

  • docs/analyze/01-project-overview.md
  • docs/analyze/02-architecture-and-code-quality.md
  • docs/analyze/03-risks-and-recommendations.md
  • docs/analyze/04-api-endpoint-list.md (only if API exists)

Systematically scan the project, perform a deep analysis, turn your findings into 3 (4 if an API exists) separate reports, and save them to the docs/analyze/ folder.

Script paths: <skill-dir> is the directory that contains this SKILL.md (for example .claude/skills/project-analyzer, .agents/skills/project-analyzer, or skills/project-analyzer inside this repository). Run the scripts from the root of the project being analyzed. If the scripts cannot run (no bash, restricted sandbox), perform the same checks manually with the available file and search tools.

Report language: Write the reports in the language the user is using. For Turkish, use these file names and translate headings accordingly:

EnglishTurkish
01-project-overview.md01-proje-genel-bakis.md
02-architecture-and-code-quality.md02-mimari-ve-kod-kalitesi.md
03-risks-and-recommendations.md03-riskler-ve-oneriler.md
04-api-endpoint-list.md04-api-endpoint-listesi.md

Core rule: Do not perform a superficial review. Actually look at the code, read the files, and provide examples for every heading. The report must contain concrete findings — do not make generic assessments.


PHASE 1 — Project Discovery (Do This First)

Get to know the project before the analysis starts. Execute the discovery script:

bash <skill-dir>/scripts/phase1_discovery.sh

Note the following after discovery:

  • Main language(s) and framework(s)
  • Project type: monolith / microservice / monorepo / library
  • Package manager
  • Test presence: does it exist, is it prevalent?
  • API type: REST / GraphQL / gRPC / WebSocket / none

PHASE 2 — Deep Analysis

Dive into the code in each category, read it, and extract concrete findings. Execute the analysis script:

bash <skill-dir>/scripts/phase2_analysis.sh

Review the outputs of this script carefully. The script outputs details regarding:

  • Architecture (folder structures, large files, circular dependencies clues)
  • Code Quality (TODOs, any usage in TS, console logs, duplicate functions, error handling)
  • Security (hardcoded secrets, committed .envs, raw SQL injection risks, vulnerable deps, auth middleware)
  • Performance (N+1 query risks, large bundles, caching, async/await usages)
  • Tests (test files layout, config presence)
  • APIs (REST endpoints, GraphQL, Swagger files)

PHASE 3 — Report Generation

Create the docs/analyze/ folder and save the 3 (or 4) reports.

mkdir -p docs/analyze

REPORT 1: 01-project-overview.md

# Project Overview

> Analysis Date: [DATE]
> Analyzed by: Project Analyzer Skill v1.0

## Summary Card

| Feature | Value |
|---------|-------|
| Project name | [package.json name or folder name] |
| Project type | [Monolith / Microservice / Monorepo / Library] |
| Main language | [TypeScript / Python / Go / ...] |
| Framework | [Hono / FastAPI / Express / Next.js / ...] |
| Package manager | [bun / pnpm / yarn / pip / ...] |
| Total source files | [N] |
| Total lines of code | [N] |
| Test coverage | [Yes / No / Partial] |
| API type | [REST / GraphQL / gRPC / None] |
| Last commit | [date] |
| Active developers | [N people] |

## Project Purpose

[summary from package.json description or README — explain in your own words]

## Folder Structure

[show a clean tree from the find output, explain the purpose of each folder in one line]

## Technology Stack

### Production Dependencies
[List each important package and write its purpose]

### Development Tools
[List test, lint, and build tools]

## Development Process Indicators

| Indicator | Value | Comment |
|-----------|-------|---------|
| Commits last 3 months | [N] | [active / low / none] |
| Test / source ratio | [%N] | [good / moderate / inadequate] |
| Number of TODOs | [N] | [clean / caution / problematic] |
| Documentation | [Yes / Partial / No] | |

## Strengths

[Write about what is genuinely good — be concrete, provide code references]

## Weaknesses

[Concrete issues — not "generally bad", but "there is a Y issue in file X"]

REPORT 2: 02-architecture-and-code-quality.md

# Architecture and Code Quality Analysis

## Architecture Assessment

### Layer Structure
[Are there layers? Controller/Service/Repository separation? Give concrete examples]

### Dependency Management
[Is there circular dependency? Is the import chain logical?]

### Modularity Score
[ ⭐⭐⭐⭐⭐ ] — [justification]

## Code Quality Metrics

| Metric | Value | Status |
|--------|-------|--------|
| Largest file | [file name: N lines] | [✅ / ⚠️ / 🔴] |
| 'any' usage (TS) | [N found] | [✅ / ⚠️ / 🔴] |
| TODO/FIXME count | [N] | [✅ / ⚠️ / 🔴] |
| console.log (prod) | [N] | [✅ / ⚠️ / 🔴] |
| try/catch ratio | [N try, N catch] | [✅ / ⚠️ / 🔴] |

## Files Requiring Attention

[List the largest, most complex files and those with the most TODOs — explain why they are risky]

### [file-name.ts] — [N lines]
> Reason for attention: [explanation]
> Recommendation: [what should be done]

## Duplicate Code (DRY Violations)

[Is similar logic written in different places? Concrete examples]

## Error Handling Quality

[Is try/catch prevalent? Are error messages meaningful? Unhandled promise rejection risk?]

## Type Safety (TypeScript)

[Usage of 'any', is strict mode enabled, are type definitions complete?]

## Overall Code Quality Score

Architecture : [1-10] / 10 Readability : [1-10] / 10
Maintainability : [1-10] / 10 Test Coverage : [1-10] / 10 ───────────────────────── Overall : [average] / 10


## Best Written Sections

[Parts of the code that are genuinely good — give concrete examples]

## Sections Most in Need of Improvement

[Concrete, prioritized improvement suggestions — state the estimated effort for each]

REPORT 3: 03-risks-and-recommendations.md

# Risks and Recommendations

## Risk Matrix

| Risk | Category | Impact | Probability | Priority |
|------|----------|--------|-------------|----------|
| [risk name] | [Security/Performance/Tech Debt/Operations] | [High/Medium/Low] | [H/M/L] | [🔴/🟡/🟢] |

## 🔴 Critical Risks (Immediate Action Required)

### [Risk Name]
**Category:** [Security / Performance / ...]
**Location:** [file:line or module]
**Issue:** [concrete explanation]
**Evidence:** [grep output or code example]
**Fix:** [step-by-step what needs to be done]
**Estimated Effort:** [1 hour / 1 day / 1 week]

## 🟡 Significant Risks (Address in the Short Term)

[Same format — 3-5 risks]

## 🟢 Improvement Recommendations (Long Term)

[Same format — 3-5 recommendations]

## Security Analysis

### Positive Findings
[Good security practices — concrete]

### Concerning Findings
[Concrete security vulnerabilities or risks]

## Performance Analysis

### Bottleneck Candidates
[N+1 query, heavy package, lack of cache, async issues]

### Optimization Opportunities
[Concrete recommendations and expected gains]

## Technical Debt Inventory

| Debt | File/Module | Estimated Effort | Priority |
|------|-------------|------------------|----------|
| [explanation] | [location] | [effort] | [🔴/🟡/🟢] |

## Proposed Action Plan

### This Week
1. [most critical, concrete step]
2. [...]

### This Month
1. [important improvements]
2. [...]

### Long Term (3-6 Months)
1. [architectural improvements]
2. [...]

## Overall Health Score

Security : [1-10] / 10 Performance : [1-10] / 10 Maintainability : [1-10] / 10 Test Coverage : [1-10] / 10 Documentation : [1-10] / 10 ───────────────────────────── Project Health : [average] / 10


REPORT 4: 04-api-endpoint-list.md (Only if API Exists)

# API Endpoint List

> Generated automatically by code scanning.
> Date: [DATE]

## Summary

| Metric | Value |
|--------|-------|
| Total endpoints | [N] |
| GET | [N] |
| POST | [N] |
| PUT/PATCH | [N] |
| DELETE | [N] |
| Requires Auth | [N] (estimated) |
| Documented | [N] |

## Endpoint Catalog

### [/api/auth] — Authentication
| Method | Path | Description | Auth | Source File |
|--------|------|-------------|------|-------------|
| POST | /api/auth/register | User registration | ❌ | src/routes/auth.ts:12 |
| POST | /api/auth/login | Login | ❌ | src/routes/auth.ts:28 |
| POST | /api/auth/logout | Logout | ✅ | src/routes/auth.ts:45 |
| GET | /api/auth/me | Current user | ✅ | src/routes/auth.ts:58 |

### [/api/...] — [other groups]
[same format — group the endpoints]

## Auth Requirements Analysis

[Which endpoints are protected, which are open? Are there missing auth rules?]

## Missing Documentation

[Endpoints without Swagger/OpenAPI definitions]

## Security Notes

[Is rate limit missing? Is there input validation? How are CORS settings?]

## Recommendations

[OpenAPI spec generation, versioning strategy, missing endpoints]

PHASE 4 — Save Files

# Create the directory
mkdir -p docs/analyze

# Save the reports
# (Save each report separately using the write_file tool)

# Show summary
echo "✅ Analysis complete"
echo "📁 docs/analyze/"
ls -la docs/analyze/

Quality Control

Perform the following checks before saving each report:

□ No generic statements — every sentence is based on concrete data or findings
□ Code references exist — "File X line N", "Function Y has problem Z"
□ Numeric metrics are real (from grep/wc output)
□ Risk priorities are logical — not everything is red
□ Recommendations are actionable — not "improve code", but "do X"
□ Scores are justified — why 7/10? because...
□ API report: source file reference exists for each endpoint

Error Scenarios

If not a Git repo: → Skip Git statistics, analyze everything else.

Very large project (>50K lines): → Take a sample from each category, state this clearly in the report.

If no tests exist: → Report test coverage as 0%, add a test strategy to the recommendations section.

If API detection is unclear: → Write "API could not be detected", do not generate the 4th report — list suspicious files instead.

Permission error: → Skip inaccessible files, note it in the report.


When to Skip

  • If no project directory is provided and the user is just asking a general question.
  • If there is no code to analyze (empty project, only configuration files).

🔗 Next Steps & Handoffs

<!-- BEGIN GENERATED: handoffs (generated from the ecosystem workflow map by build_ecosystems.py — do not edit by hand) -->

Ecosystem: @ecosystem-orchestration — Agent Orchestration.

Workflows:

  • Repository Change Flow (orch-code-change, step 1 of 3): next → @plan-implement-verify-recover.

Handoff contract: pass results to the next skill through docs/analyze/01-project-overview.md with the fields skill, workflow, created_at, inputs, summary, and next (the handoff contract of @ecosystem-orchestration). If a next skill is not installed, continue with its step from the ecosystem map, or install it with npx skills add fatih-developer/fth-skills --skill <name> after the user agrees.

<!-- END GENERATED: handoffs -->

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