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Agent skill

Debug

by WellApp-ai in WellApp-ai/Well

Systematic debugging with MCP integration, auto-invoke from qa-commit, Phase 7 Harden

MITAuto-check passedDevelopment

Install Debug

skills CLI
$ npx skills add WellApp-ai/Well --skill debug -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install WellApp-ai/Well debug --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/WellApp-ai/Well.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cursor-rules/skills/debug .claude/skills/debug && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
debug
GitHub stars
345
Token cost
~2.5k tokens
SKILL.md length
697 words
Files
1
Skills in repo
36
Repo updated
First seen
Licence
MIT

At a glance

Systematic debugging with MCP integration, auto-invoke from qa-commit, Phase 7 Harden

  • Works in 10 steps: Context Loading (Auto-Invoke Only) → 5: Jidoka Escalation Check (NEW) → Gather Information (Enhanced) → …
  • Tasks that involve Debugging
  • SKILL.md covers When to Use, Modes, The Enhanced Flow and Phase 0: Context Loading…, plus 6 more sections
  • Calls npm and git

What it does

Debug is an agent skill from WellApp-ai/Well. Systematic debugging with MCP integration, auto-invoke from qa-commit, Phase 7 Harden

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Debugging and MCP servers. It works with Model Context Protocol. The repository describes itself as: No more Sundays on Finance. We build the infrastructure that retrieves, processes, and routes your financial and business data to your FinOps stack, so founders can ship, not… The licence is MIT.

When your agent uses it

  • Tasks that involve Debugging
  • Tasks that involve MCP servers

Example prompts

  • “/debug”

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. Context Loading (Auto-Invoke Only)
  2. 5: Jidoka Escalation Check (NEW)
  3. Gather Information (Enhanced)
  4. Reproduce (Enhanced)
  5. Isolate (Enhanced with Known Issues DB)
  6. Diagnose
  7. Fix (Enhanced)
  8. Verify (Enhanced)
  9. Harden (NEW)
  10. Update Jidoka Counters (NEW)

What it can do on your machine

Read from SKILL.md and the folder at commit c740217. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm and git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Debug loads about 2.5k tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 697 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~23
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from WellApp-ai/Well at commit c740217, republished under its MIT licence (© WellApp-ai). 697 words, ~2,507 tokens.

Download SKILL.mdSave it as .claude/skills/debug/SKILL.md (or your agent's skills folder).
name
debug
description
Systematic debugging with MCP integration, auto-invoke from qa-commit, Phase 7 Harden

Debug Skill

Diagnose and fix issues systematically. Enhanced with MCP integrations for deeper analysis and automatic regression test generation.

When to Use

  • Auto-invoked by qa-commit skill on RED verdict
  • Error messages appearing in console/terminal
  • Feature not working as expected
  • Build/runtime failures
  • "Something is broken" situations

Modes

ModeTriggerContext Provided
Autoqa-commit RED verdictFailed G#N/AC#N, error messages
ManualUser invokesUser describes issue

The Enhanced Flow

Phase 0: Context Loading (if auto-invoked)
    ↓
Phase 0.5: Jidoka Escalation Check ──→ [Tier 2/3] ──→ ESCALATE to human
    ↓ [Tier 1]
Phase 1: Gather (ReadLints, Browser MCP, Context7)
    ↓
Phase 2: Reproduce (Browser MCP)
    ↓
Phase 3: Isolate (Known Issues DB query)
    ↓
Phase 4: Diagnose
    ↓
Phase 5: Fix
    ↓
Phase 6: Verify ──→ [FAIL] ──→ Phase 8 ──→ Phase 0.5
    ↓ [PASS]
Phase 7: Harden (generate regression test)
    ↓
Phase 8: Update Jidoka Counters (reset on success)

Phase 0: Context Loading (Auto-Invoke Only)

When invoked from qa-commit, receive context:

markdown
## Debug Context (from qa-commit)

**Failed Criteria:**
- [G#N or AC#N]: [Description]

**Verification Report:**
- ReadLints errors: [list]
- Shell errors: [list]
- Browser errors: [list if applicable]

**Expected Behavior:**
[From QA Contract]

**Actual Behavior:**
[Observed during verification]

Skip this phase if manually invoked.

Phase 0.5: Jidoka Escalation Check (NEW)

Before attempting fix, check escalation tier to determine if human intervention is needed.

Track Error History

Maintain error_history across debug invocations:

FieldDescription
error_signatureHash of error type + location
countTimes this exact error seen
fixes_attemptedList of fix descriptions
Tier Evaluation
TierConditionAction
Tier 1error_count < 3Continue to Phase 1 (normal debug)
Tier 2error_count >= 3 (same error)ESCALATE to human
Tier 3total_errors >= 5 (any)ESCALATE to human
Tier 2/3 Escalation Output

If escalation triggered, skip Phases 1-7 and output:

R | [Feature] | AGENT | JIDOKA STOP
---
Same error detected [N] times:
> [Error message]

Attempted fixes:
1. [Fix 1] - Failed: [why]
2. [Fix 2] - Failed: [why]
3. [Fix 3] - Failed: [why]

Options:
A. Try different approach - [describe alternative]
B. Skip this commit, continue to next
C. Pause session, investigate manually
D. Abort feature, reassess scope

---
Reply with A, B, C, or D

Invoke decision-capture skill with escalation context.

Escalation Resolution

On user response:

  • A: Reset error_count for this signature, apply new approach
  • B: Mark commit as SKIPPED, proceed to next
  • C: End session, invoke session-status for final metrics
  • D: End session with ABANDONED outcome

Phase 1: Gather Information (Enhanced)

1.1 ReadLints Integration

Use Cursor's ReadLints tool on affected files:

ReadLints:
  paths: [affected files from context]

Categorize:

  • Errors → Primary suspects
  • Warnings → Secondary investigation
  • Related files → Expand scope if needed
1.2 Browser MCP Deep Scan

For frontend issues, use Browser MCP:

browser_navigate: [affected URL]
browser_snapshot: Get current DOM state
browser_console_messages: All errors/warnings
browser_network_requests: API failures

Extract:

  • Console errors with stack traces
  • Failed network requests with status codes
  • DOM state anomalies
1.3 Context7 Error Lookup

Identify libraries involved and query for error patterns:

Context7 MCP:
1. resolve-library-id: libraryName = "[library from stack trace]"
2. get-library-docs: topic = "[error message keywords]", mode = "info"

Look for:

  • Known issues with the library
  • Common error patterns
  • Recommended fixes
1.4 Standard Gathering
  • Get full error message/stack trace
  • Check server logs if backend issue
  • Identify when issue started (recent changes?)

Phase 2: Reproduce (Enhanced)

2.1 Document Steps
markdown
## Reproduction Steps

1. Navigate to: [URL]
2. Action: [What triggers the issue]
3. Expected: [What should happen]
4. Actual: [What actually happens]
2.2 Browser MCP Reproduction
browser_navigate: [starting URL]
browser_click: [trigger element]
browser_type: [if input needed]
browser_take_screenshot: Capture failure state
browser_network_requests: Capture API calls
2.3 Capture Evidence
  • Screenshot at failure point
  • Console log at failure
  • Network request/response

Phase 3: Isolate (Enhanced with Known Issues DB)

3.1 Query Known Issues Database

Before deep investigation, check if this is a known issue:

Notion MCP:
API-query-database:
  database_id: "[KNOWN_ISSUES_DB_ID]"
  filter:
    property: "Error Pattern"
    rich_text:
      contains: "[error keywords]"

If match found:

markdown
## Known Issue Match

**Pattern:** [Error pattern from DB]
**Root Cause:** [From DB]
**Fix Pattern:** [From DB]
**Occurrences:** [N] times

Applying known fix...

→ Skip to Phase 5 with known fix.

If no match: → Continue to Phase 4.

3.2 Standard Isolation
  • Trace error to specific file/line
  • Check recent git changes: git log -5 --oneline
  • Search for related code: SemanticSearch, Grep

Phase 4: Diagnose

4.1 Root Cause Analysis

Read relevant code with context:

Read: [file with error]
SemanticSearch: "How is [function] supposed to work?"
Show full SKILL.md (284 more words)Show less
4.2 Check Common Issues
  • Type mismatches
  • Null/undefined access
  • Async timing issues
  • Missing dependencies
  • State management bugs
  • API contract mismatches
4.3 Hypothesis Formation
markdown
## Diagnosis

**Root Cause:** [What's causing the issue]

**Evidence:**
- [Evidence 1]
- [Evidence 2]

**Proposed Fix:** [What needs to change]

Phase 5: Fix (Enhanced)

5.1 Pattern Compliance

Before implementing fix:

  1. Invoke design-context skill (silent)
  2. Check Context7 for library best practices
  3. Ensure fix follows existing patterns
5.2 Implement Fix
  • Propose minimal fix
  • Explain why fix works
  • Wait for user approval before implementing
5.3 Apply Fix

Make the code changes.

Phase 6: Verify (Enhanced)

6.1 Technical Verification
bash
npm run typecheck
npm run lint
npm run test -- --grep "[related tests]"
6.2 Re-run qa-commit

For the specific failed criteria:

markdown
## Re-verification

Re-running qa-commit for:
- [G#N or AC#N that failed]

Result: [PASS/FAIL]
6.3 Outcome

If PASS: Continue to Phase 7 (Harden) If FAIL: Return to Phase 3 (Isolate) with new information

Phase 7: Harden (NEW)

Prevent regression by generating tests and updating knowledge base.

7.1 Generate Regression Test

Create test that would catch this issue:

For Backend (G#N):

typescript
// Regression test: [issue description]
// Debug session: [date]
it('should not [bug behavior] when [condition]', async () => {
  // Reproduction steps
  const result = await [action that caused bug];
  expect(result).not.toBe([buggy behavior]);
  expect(result).toBe([correct behavior]);
});

For Frontend (AC#N):

typescript
// Regression test: [issue description]
test('should handle [edge case]', async ({ page }) => {
  // Reproduction steps
  await page.goto('[URL]');
  await page.click('[trigger]');
  await expect(page.locator('[element]')).toBeVisible();
});
7.2 Invoke test-hardening
markdown
Invoking test-hardening skill for regression test...
7.3 Update Known Issues (if novel)

If this was a new issue pattern:

Notion MCP:
API-create-page:
  parent: { database_id: "[KNOWN_ISSUES_DB_ID]" }
  properties:
    Error Pattern: "[Error message pattern]"
    Root Cause: "[What caused it]"
    Fix Pattern: "[How to fix]"
    Library: [relation if applicable]
    Occurrences: 1
7.4 Capture Patine (if significant)

If this reveals a pattern worth remembering:

markdown
Invoking decision-capture skill...
"Learned: [pattern] causes [issue]. Fix: [approach]."

Phase 8: Update Jidoka Counters (NEW)

Update escalation counters based on fix outcome.

On GREEN (fix successful)
  • Reset error_count for this signature to 0
  • Clear fixes_attempted list
  • Log success in session metrics
  • Invoke session-status to update muda tracking
markdown
## Jidoka Counter Reset

Error signature: [hash]
Previous count: [N]
New count: 0
Status: RESOLVED
On RED (fix failed)
  • Increment error_count for this signature
  • Append fix description to fixes_attempted
  • Return to Phase 0.5 for tier check
markdown
## Jidoka Counter Update

Error signature: [hash]
Count: [N] → [N+1]
Fix attempted: [description]
Next: Re-evaluate escalation tier

Output Format

markdown
## Debug Report

### Issue
[Brief description]

### Root Cause
[What caused it]

### Fix Applied
[What was changed]

### Verification
- TypeCheck: PASS
- Lint: PASS
- Tests: PASS
- qa-commit: GREEN

### Hardening
- Regression test: [Created/Skipped]
- Known Issues: [Added/Existing]
- Patine: [Captured/Skipped]

**Status:** RESOLVED

MCP Tools Used

ToolPhasePurpose
ReadLints1Get lint/type errors
Browser MCP1, 2Console, network, DOM
Context71Library error patterns
Notion MCP3, 7Known Issues database
Shell6Run tests, typecheck
SemanticSearch3, 4Find related code
Grep3Search for patterns

Invocation

  • Auto: Invoked by qa-commit on RED verdict
  • Manual: "use debug skill"

© WellApp-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in cursor-rules/skills/debug of WellApp-ai/Well.

Open the folder on GitHubat commit c740217

Compare with similar skills

Debug next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Debug compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Debug this skillWellApp-ai/Well345—~2.5kAutomated safety check: PassMIT
Misakanet Failure MemoryIkalus1988/MisakaNet524—~1.9kAutomated safety check: PassApache-2.0
Gearcoleco Debuggingdrhelius/Gearcoleco141—~3.5kAutomated safety check: PassGPL-3.0
MCP Debuggerdebugmcp/mcp-debugger171—~3.8kAutomated safety check: PassMIT
Vscode MCP Architecturetjx666/vscode-mcp106—~1.5kAutomated safety check: PassCustom licence
SlintMoosync/Moosync259—~2.4kAutomated safety check: PassGPL-3.0

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Works with

Categories

Questions about Debug

What does Debug do?

Systematic debugging with MCP integration, auto-invoke from qa-commit, Phase 7 Harden. Debug is an agent skill from WellApp-ai/Well.

When should I use Debug?

Debug fits situations like: tasks that involve Debugging; tasks that involve MCP servers.

How do I install Debug in Claude Code?

Run `npx skills add WellApp-ai/Well --skill debug -a claude-code`. Or copy the skill folder (cursor-rules/skills/debug in WellApp-ai/Well) into .claude/skills/debug in your project. Claude Code loads it when a task matches its description.

How do I install Debug in Codex?

Run `npx skills add WellApp-ai/Well --skill debug -a codex`. Or copy the skill folder (cursor-rules/skills/debug in WellApp-ai/Well) into .agents/skills/debug in your project. Codex loads it when a task matches its description.

Can I use Debug in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add WellApp-ai/Well --skill debug -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/debug, .gemini/skills/debug, .github/skills/debug and .opencode/skills/debug in your project.

What does Debug need to run?

Going by SKILL.md and its folder, Debug needs the command-line tools its instructions call (npm and git).

Does Debug access the network?

SKILL.md contains no URLs. Its commands use npm and git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Debug safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Debug use?

Debug is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Debug use?

About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Debug?

Skills that share tags, products or a category with Debug: Misakanet Failure Memory (Ikalus1988/MisakaNet, 524 stars), Gearcoleco Debugging (drhelius/Gearcoleco, 141 stars), MCP Debugger (debugmcp/mcp-debugger, 171 stars) and Vscode MCP Architecture (tjx666/vscode-mcp, 106 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Debug?

WellApp-ai (a GitHub organization) maintains it in WellApp-ai/Well, which has 345 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on September 30, 2026.

Source: WellApp-ai/Well on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.