fix

parcadei/continuous-claude-v3 · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill fix
0 commentsdiscussion
summary

Workflow orchestrator for bug investigation and resolution. Chains specialized skills based on issue scope.

skill.md

Fix

Workflow orchestrator for bug investigation and resolution. Chains specialized skills based on issue scope.

Usage

/fix <scope> [options] [description]

Question Flow (No Arguments)

If the user types just /fix with no or partial arguments, guide them through this question flow. Use AskUserQuestion for each phase.

Phase 0: Workflow Selection

question: "What would you like to fix?"
header: "Fix type"
options:
  - label: "Help me choose (Recommended)"
    description: "I'll ask questions to pick the right fix workflow"
  - label: "Bug - something is broken"
    description: "Chain: investigate → diagnose → implement → test → commit"
  - label: "Hook - Claude Code hook issue"
    description: "Chain: debug-hooks → hook-developer → implement → test"
  - label: "Dependencies - import/package errors"
    description: "Chain: preflight → research → plan → implement → qlty-check"
  - label: "PR Comments - address reviewer feedback"
    description: "Chain: github-search → research → plan → implement → commit"

Mapping:

  • "Help me choose" → Continue to Phase 1-4 questions
  • "Bug" → Set scope=bug, skip to Phase 2 (issue details)
  • "Hook" → Set scope=hook, skip to Phase 2 (issue details)
  • "Dependencies" → Set scope=deps, skip to Phase 2 (issue details)
  • "PR Comments" → Set scope=pr-comments, skip to Phase 2 (issue details)

If Answer is Unclear (via "Other"):

question: "I want to understand what kind of fix you need. Did you mean..."
header: "Clarify"
options:
  - label: "Help me choose"
    description: "Not sure - guide me through questions"
  - label: "Bug - something is broken"
    description: "Code isn't working as expected"
  - label: "Hook - Claude Code hook issue"
    description: "Hooks not firing or producing wrong output"
  - label: "Neither - let me explain differently"
    description: "I'll describe my issue"

Phase 1: Issue Type

question: "What kind of issue are you dealing with?"
header: "Issue type"
options:
  - label: "Something is broken/not working"
    description: "Bug in the code"
  - label: "Claude Code hook not firing"
    description: "Hook-specific debugging"
  - label: "Import/dependency errors"
    description: "Package or module issues"
  - label: "Need to address PR feedback"
    description: "Reviewer comments to fix"

Mapping:

  • "Something broken" → bug scope
  • "Hook not firing" → hook scope
  • "Import errors" → deps scope
  • "PR feedback" → pr-comments scope

Phase 2: Issue Details

question: "Can you describe the issue?"
header: "Details"
options: []  # Free text - user describes the problem

Capture the error message, unexpected behavior, or PR link.

Phase 3: Investigation Depth

question: "How should I investigate?"
header: "Investigation"
options:
  - label: "Diagnose and fix"
    description: "Find the problem and implement a fix"
  - label: "Diagnose only (dry run)"
    description: "Just tell me what's wrong, don't change code"
  - label: "Quick fix"
    description: "I know the issue, just fix it fast"

Mapping:

  • "Diagnose only" → --dry-run
  • "Quick fix" → skip investigation, go straight to spark agent

Phase 4: Testing & Commit

question: "After fixing, should I..."
header: "After fix"
multiSelect: true
options:
  - label: "Write a regression test"
    description: "Prevent this bug from recurring"
  - label: "Commit the fix"
    description: "Create a git commit"
  - label: "Just fix, nothing else"
    description: "I'll handle tests and git"

Mapping:

  • No "regression test" → --no-test
  • No "commit" → --no-commit

Summary Before Execution

Based on your answers, I'll run:

**Scope:** bug
**Issue:** "Login button not responding on Safari"
**Chain:** sleuth (investigate) → spark (fix) → arbiter (test) → commit
**Options:** (none)

Proceed? [Yes / Adjust settings]

Scopes

Scope Chain Description
bug debug -> implement_task -> test-driven-development -> commit General bug fix workflow
hook debug-hooks -> hook-developer -> implement_task -> test hook Hook-specific debugging
deps dependency-preflight -> oracle -> plan-agent -> implement_plan -> qlty-check Dependency issues
pr-comments github-search -> research-codebase -> plan-agent -> implement_plan -> commit Address PR feedback

Options

Option Effect
--no-test Skip regression test creation
--dry-run Diagnose only, don't implement fix
--no-commit Don't auto-commit the fix

Workflow

Phase 1: Parse Arguments

# Parse scope and options
SCOPE="${1:-bug}"
NO_TEST=false
DRY_RUN=false
NO_COMMIT=false

for arg in "$@"; do
  case $arg in
    --no-test) NO_TEST=true ;;
    --dry-run) DRY_RUN=true ;;
    --no-commit) NO_COMMIT=true ;;
  esac
done

Phase 2: Investigation (Parallel)

Spawn sleuth agent for parallel investigation:

Task(
  subagent_type="sleuth",
  prompt="""
  Investigate this issue in parallel:

  1. **Logs**: Check recent logs for errors
     - Application logs
     - System logs if relevant
     - Build/test output

  2. **Database State** (if applicable):
     - Check for stuck/invalid records
     - Verify schema matches expectations

  3. **Git State**:
     - Recent commits that might relate
     - Uncommitted changes
     - Current branch context

  4. **Runtime State**:
     - Running processes
     - Port conflicts
     - Environment variables

  Issue description: {user_description}

  Return structured findings with evidence.
  """
)

Phase 3: Diagnosis Report

Present findings to user:

## Diagnosis Report

### Scope: {scope}

### Evidence Found

**Logs:**
- [Finding with timestamp/line reference]

**Database:**
- [Finding with table/query reference]

**Git State:**
- [Recent relevant commits]
- [Uncommitted changes]

**Runtime:**
- [Process/port findings]

### Root Cause Analysis

**Primary Hypothesis:** [Most likely cause based on evidence]

**Supporting Evidence:**
1. [Evidence 1]
2. [Evidence 2]

**Alternative Hypotheses:**
- [Alternative 1]: [Why less likely]

### Proposed Fix

**Approach:** [How to fix]

**Files to Modify:**
- `path/to/file.ts:123` - [Change description]

**Risk Assessment:** [Low/Medium/High] - [Why]

---

**Proceed with fix?** (yes/no/modify approach)

Phase 4: Human Checkpoint (Diagnosis)

REQUIRED: Wait for user confirmation before implementing.

how to use fix

How to use fix on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add fix
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/parcadei/continuous-claude-v3 --skill fix

The skills CLI fetches fix from GitHub repository parcadei/continuous-claude-v3 and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/fix

Reload or restart Cursor to activate fix. Access the skill through slash commands (e.g., /fix) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.450 reviews
  • Ira Gill· Dec 12, 2024

    fix reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Ren Bhatia· Dec 8, 2024

    Registry listing for fix matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ganesh Mohane· Dec 4, 2024

    Registry listing for fix matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ira Liu· Dec 4, 2024

    Useful defaults in fix — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ira Desai· Nov 27, 2024

    Solid pick for teams standardizing on skills: fix is focused, and the summary matches what you get after install.

  • Rahul Santra· Nov 23, 2024

    Solid pick for teams standardizing on skills: fix is focused, and the summary matches what you get after install.

  • Li Mensah· Nov 23, 2024

    I recommend fix for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ren Chawla· Nov 3, 2024

    We added fix from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ren Malhotra· Oct 22, 2024

    Keeps context tight: fix is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Ren Khan· Oct 18, 2024

    I recommend fix for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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