Agent skill

Systematic Debugging

by AlexAI-MCP in AlexAI-MCP/hermes-CCC

Run a disciplined multi-phase debugging loop for Claude Code tasks.

MITAuto-check passedDevelopment

Install Systematic Debugging

skills CLI
$ npx skills add AlexAI-MCP/hermes-CCC --skill systematic-debugging -a claude-code

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

GitHub CLI
$ gh skill install AlexAI-MCP/hermes-CCC systematic-debugging --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/AlexAI-MCP/hermes-CCC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/systematic-debugging .claude/skills/systematic-debugging && 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
systematic-debugging
GitHub stars
135
Token cost
~1.7k tokens
SKILL.md length
815 words
Files
1
Skills in repo
44
Repo updated
First seen
Licence
MIT

At a glance

Run a disciplined multi-phase debugging loop for Claude Code tasks.

  • Works in 10 steps: Stabilize The Report → Reproduce The Failure → Narrow The Surface Area → …
  • Failures are ambiguous
  • SKILL.md covers Purpose, Activation Signals, Debugging Phases and Phase 1: Stabilize The Report, plus 18 more sections
  • Calls git, pytest and rg

What it does

Systematic Debugging is an agent skill from AlexAI-MCP/hermes-CCC. Run a disciplined multi-phase debugging loop for Claude Code tasks. Use when failures are ambiguous, regressions are hard to localize, logs are noisy, or a bug fix must be proven rather than guessed.

Its SKILL.md is about 1.7k 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. The repository describes itself as: Hermes Agent ported to Claude Code Channel — 46 native skills, no OAuth, no external process. The licence is MIT.

When your agent uses it

  • Failures are ambiguous
  • Regressions are hard to localize
  • A bug fix must be proven rather than guessed

Example prompts

  • “/systematic-debugging”

Workflow steps

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

  1. Stabilize The Report
  2. Reproduce The Failure
  3. Narrow The Surface Area
  4. Instrument The System
  5. Generate Hypotheses
  6. Prove Or Kill Hypotheses
  7. Patch The True Cause
  8. Verify The Fix
  9. Add Regression Protection
  10. Save The Lesson

What it can do on your machine

Read from SKILL.md and the folder at commit 8107e89. 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:

    • git
    • pytest
    • rg

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

  • Network

    No URLs in SKILL.md. Its commands use 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

Systematic Debugging loads about 1.7k tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 815 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

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 AlexAI-MCP/hermes-CCC at commit 8107e89, republished under its MIT licence (© AlexAI-MCP). 815 words, ~1,697 tokens.

Download SKILL.mdSave it as .claude/skills/systematic-debugging/SKILL.md (or your agent's skills folder).
name
systematic-debugging
description
Run a disciplined multi-phase debugging loop for Claude Code tasks. Use when failures are ambiguous, regressions are hard to localize, logs are noisy, or a bug fix must be proven rather than guessed.
version
0.1.0
author
OpenAI Codex
license
MIT
metadata.category
engineering
metadata.ported_from
NousResearch Hermes Agent
metadata.tags
debugging, root-cause, regression, verification
metadata.tools
shell, tests, logs
metadata.maturity
beta

Systematic Debugging

Purpose

  • Prevent guess-driven bug fixing.
  • Turn a vague failure into a proved root cause.
  • Separate symptom collection from patching.
  • Minimize regressions while repairing defects.
  • Leave behind a reproducible explanation of the failure.

Activation Signals

  • Use this skill when the root cause is unknown.
  • Use this skill when a previous fix attempt failed.
  • Use this skill when the bug spans multiple files or layers.
  • Use this skill when logs, stack traces, or user reports disagree.
  • Use this skill when the fix must be defensible in review.

Debugging Phases

  1. Stabilize the report.
  2. Reproduce the failure.
  3. Narrow the surface area.
  4. Instrument the system.
  5. Generate hypotheses.
  6. Prove or kill hypotheses.
  7. Patch the true cause.
  8. Verify the fix.
  9. Add regression protection.
  10. Save the lesson if durable.

Phase 1: Stabilize The Report

  • Write the exact symptom in one sentence.
  • Record expected behavior.
  • Record actual behavior.
  • Capture environment assumptions.
  • Capture whether the issue is deterministic or flaky.
  • Capture whether it is new, old, or recently regressed.
  • Avoid editing code during this phase.

Phase 2: Reproduce The Failure

  • Find the smallest reproducible path.
  • Prefer automated reproduction over manual UI clicking.
  • Save the failing command when possible.
  • If no test exists, create a reproduction harness.
  • Record exact inputs, flags, and fixtures.
  • If the bug is flaky, measure frequency instead of pretending it is stable.

Phase 3: Narrow The Surface Area

  • Identify likely subsystem boundaries.
  • Diff recent changes when history is relevant.
  • Check whether the failure begins before or after I/O boundaries.
  • Compare passing and failing code paths.
  • Use binary search over scope when the change window is large.
  • Reduce the problem before adding more instrumentation.

Phase 4: Instrument The System

  • Add temporary logging only where uncertainty exists.
  • Prefer cheap inspection over permanent noisy logging.
  • Log invariant checkpoints, not everything.
  • Print or inspect the variables that decide branching behavior.
  • If async behavior is involved, log timestamps and ordering.
  • Remove temporary instrumentation after the fix unless it becomes useful observability.

Phase 5: Generate Hypotheses

  • Generate multiple plausible causes, not one favorite theory.
  • Rank hypotheses by explanatory power and test cost.
  • Prefer hypotheses that explain all observed symptoms.
  • Write down what evidence would falsify each one.
  • Do not patch based on intuition alone.

Phase 6: Prove Or Kill Hypotheses

  • Design the smallest experiment that differentiates the top hypotheses.
  • Run one high-signal experiment at a time.
  • Keep notes on what each result means.
  • Kill hypotheses aggressively when evidence contradicts them.
  • Escalate instrumentation only when the current evidence is insufficient.

Phase 7: Patch The True Cause

  • Change only the code implicated by evidence.
  • Prefer the smallest change that restores the invariant.
  • Avoid bundling unrelated cleanup in the debug patch.
  • If the bug exposed a missing boundary, add the boundary explicitly.
  • Preserve readability even for emergency fixes.

Phase 8: Verify The Fix

  • Re-run the original reproduction.
  • Re-run nearby tests.
  • Check negative cases, not just the positive happy path.
  • Validate that logs or state transitions now match expectation.
  • If the bug was flaky, run enough repetitions to earn confidence.
Show full SKILL.md (310 more words)Show less

Phase 9: Add Regression Protection

  • Add or strengthen a test that would have caught the bug.
  • Place the test at the lowest layer that reliably expresses the defect.
  • If a test is impossible, document the missing seam.
  • Prefer deterministic tests over timing-sensitive ones.

Phase 10: Save The Lesson

  • Save a durable memory entry if the issue reflects a reusable pattern.
  • Save a trajectory if the investigation is valuable for QA or training data.
  • Update docs only if the bug revealed a workflow or contract gap.

Evidence Sources

  • failing tests
  • stack traces
  • logs
  • diffs
  • recent commits
  • config files
  • environment variables
  • production reports
  • screenshots or traces

Useful Commands

bash
pytest -k "failing_case" -vv
rg "relevant_symbol|error_text" .
git diff --stat
git log --oneline -- path/to/file

Decision Rules

  • If you cannot reproduce the issue, stop calling it fixed.
  • If two symptoms disagree, debug the disagreement first.
  • If a bug appears after a refactor, compare invariants, not just syntax.
  • If instrumentation grows large, your narrowing step failed.
  • If the patch is broad, your hypothesis is still weak.

Anti-Patterns

  • editing first, explaining later
  • assuming the first stack trace frame is the root cause
  • mixing refactor work into a debug patch
  • adding logs everywhere
  • declaring victory after one pass on a flaky issue

Output Contract

Return a compact debug summary with:

  • symptom
  • reproduction
  • root cause
  • patch
  • verification
  • regression protection
  • residual risk

Example Output

markdown
Symptom: login succeeds but redirect loops back to /signin
Reproduction: pytest tests/test_auth.py::test_redirect_loop -vv
Root cause: middleware treated a partially hydrated session as unauthenticated
Patch: guard now waits for the session token before redirecting
Verification: targeted auth test passed; manual flow rechecked
Regression protection: added test for partially hydrated session state
Residual risk: none observed outside auth middleware

Failure Modes

  • no reliable reproduction
  • incomplete instrumentation
  • hypothesis chosen before evidence
  • patch fixes symptom but not cause
  • missing regression test

Recovery Moves

  • Build a reproduction harness if one does not exist.
  • Re-scope the failing boundary if evidence stays noisy.
  • Revert speculative changes and return to a known failing baseline.
  • Reduce the diff until each change has a reason.

Checklist

  1. Stabilize the report.
  2. Reproduce the failure.
  3. Narrow the surface area.
  4. Instrument selectively.
  5. Rank hypotheses.
  6. Prove or kill them.
  7. Patch the true cause.
  8. Verify with evidence.
  9. Add regression protection.
  10. Save durable lessons.

© AlexAI-MCP, 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 skills/systematic-debugging of AlexAI-MCP/hermes-CCC.

Open the folder on GitHubat commit 8107e89

Compare with similar skills

Systematic Debugging 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.

Systematic Debugging compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Systematic Debugging this skillAlexAI-MCP/hermes-CCC135—~1.7kAutomated safety check: PassMIT
Trellis Session Insightmindfold-ai/Trellis15k4 repos~1.7kAutomated safety check: PassAGPL-3.0
Native Data FetchingCherryHQ/cherry-studio-app4k6 repos~2.9kAutomated safety check: NotesMIT
Aoti Debugpytorch/pytorch104k1 repos~1.7kAutomated safety check: PassCustom licence
Herdr Throwaway Reproductionherdrdev/herdr43k—~2.4kAutomated safety check: PassApache-2.0
Systematic Debuggingultralisp/ultralisp25851 repos~2.4kAutomated safety check: PassNone

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Categories

Questions about Systematic Debugging

What does Systematic Debugging do?

Run a disciplined multi-phase debugging loop for Claude Code tasks. Systematic Debugging is an agent skill from AlexAI-MCP/hermes-CCC. Run a disciplined multi-phase debugging loop for Claude Code tasks.

When should I use Systematic Debugging?

Systematic Debugging fits situations like: failures are ambiguous; regressions are hard to localize; A bug fix must be proven rather than guessed.

How do I install Systematic Debugging in Claude Code?

Run `npx skills add AlexAI-MCP/hermes-CCC --skill systematic-debugging -a claude-code`. Or copy the skill folder (skills/systematic-debugging in AlexAI-MCP/hermes-CCC) into .claude/skills/systematic-debugging in your project. Claude Code loads it when a task matches its description.

How do I install Systematic Debugging in Codex?

Run `npx skills add AlexAI-MCP/hermes-CCC --skill systematic-debugging -a codex`. Or copy the skill folder (skills/systematic-debugging in AlexAI-MCP/hermes-CCC) into .agents/skills/systematic-debugging in your project. Codex loads it when a task matches its description.

Can I use Systematic Debugging 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 AlexAI-MCP/hermes-CCC --skill systematic-debugging -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/systematic-debugging, .gemini/skills/systematic-debugging, .github/skills/systematic-debugging and .opencode/skills/systematic-debugging in your project.

What does Systematic Debugging need to run?

Going by SKILL.md and its folder, Systematic Debugging needs the command-line tools its instructions call (git, pytest and rg).

Does Systematic Debugging access the network?

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

Is Systematic Debugging 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 Systematic Debugging use?

Systematic Debugging is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Systematic Debugging use?

About 1.7k tokens (SKILL.md is roughly 6.8k 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 Systematic Debugging?

Skills that share tags, products or a category with Systematic Debugging: Trellis Session Insight (mindfold-ai/Trellis, 15k stars), Native Data Fetching (CherryHQ/cherry-studio-app, 4k stars), Aoti Debug (pytorch/pytorch, 104k stars) and Herdr Throwaway Reproduction (herdrdev/herdr, 43k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Systematic Debugging?

AlexAI-MCP (a GitHub user) maintains it in AlexAI-MCP/hermes-CCC, which has 135 GitHub stars. The repository holds 44 skills in this directory. The repository was last updated on April 8, 2026.

Source: AlexAI-MCP/hermes-CCC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.