systematic-debugging

systematic-debugging is a skill for Claude Code, Codex from vstorm-co/pydantic-deepagents. It costs 13 tokens per session (711 once invoked), scanned A, original, MIT.

A step-by-step method for finding and fixing software bugs. It starts by reproducing the failure, then narrows down its cause and checks that the fix works.

In plain words
What is it for?
Use it to investigate compiler errors, crashes, incorrect results, timeouts, and hangs by collecting evidence, isolating the smallest failing case, and verifying the repair.
Why use it?
It reduces guesswork and helps prevent repeated trial and error when an error is hard to understand.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to investigate compiler errors, crashes, incorrect results, timeouts, and hangs by collecting evidence, isolating the smallest failing case, and verifying the repair.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vstorm-co/pydantic-deepagents/systematic-debugging
About the project

Pydantic Deep Agents is a self-hosted terminal AI assistant and Python framework for building coding, research, and other AI agents. It gives agents tools such as file access, shell commands, planning, memory, sub-agents, sandboxed execution, and MCP connections, and supports different models.

vstorm-co/pydantic-deepagents · 1,103 stars · on GitHub · vstorm-co.github.io

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add vstorm-co/pydantic-deepagents --skill systematic-debugging
Clone the repo
git clone --depth 1 https://github.com/vstorm-co/pydantic-deepagents

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for systematic-debugging

README.md
[![agentmods](https://agentmods.dev/badge/skills/vstorm-co/pydantic-deepagents/systematic-debugging/github.svg)](https://agentmods.dev/skills/vstorm-co/pydantic-deepagents/systematic-debugging)
Your own site
<a href="https://agentmods.dev/skills/vstorm-co/pydantic-deepagents/systematic-debugging"><img src="https://agentmods.dev/badge/skills/vstorm-co/pydantic-deepagents/systematic-debugging/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for systematic-debugging

Your own site · 80×15
<a href="https://agentmods.dev/skills/vstorm-co/pydantic-deepagents/systematic-debugging"><img src="https://agentmods.dev/badge/skills/vstorm-co/pydantic-deepagents/systematic-debugging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 13 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 711 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00013 $0.00711
Opus 5.5 $0.00005 $0.00284
Sonnet 5.5 $0.00003 $0.00142
Haiku 4.5 $0.00001 $0.00071

Measured 1mo ago against content hash 1f65a08595e6, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

systematic-debugging scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

apps/cli/skills/systematic-debugging/SKILL.md · 89 lines

How it starts

The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Systematic Debugging

A structured approach to finding and fixing bugs.

The Debugging Loop

1. REPRODUCE → 2. ISOLATE → 3. DIAGNOSE → 4. FIX → 5. VERIFY

Never skip steps. Never guess-and-check repeatedly.

Step 1: Reproduce

  • Run the exact command that fails
  • Capture FULL output (stdout AND stderr)
  • Note: exit code, error message, stack trace
  • If intermittent, identify what changes between runs

Step 2: Isolate

  • What's the MINIMAL input that triggers the error?
  • Which specific line/function fails? (read the traceback bottom-up)
  • Is it a compile error, runtime error, or wrong output?
  • Does it fail on all inputs or specific ones?

Step 3: Diagnose

Read the error message carefully

Error type Where to look
Compile error The FIRST error (later ones are often cascading)
Segfault Last function in the stack trace, check array bounds and null pointers
Python traceback The innermost frame (bottom), but also check the middle for context
Wrong output Diff expected vs actual: diff <(expected) <(actual)
Timeout/hang Is it an infinite loop? Deadlock? I/O bound? Add a timer or counter

Add minimal instrumentation

  • C: fprintf(stderr, "reached checkpoint %d\n", __LINE__);
  • Python: print(f"DEBUG: {var=}", file=sys.stderr)
  • Check intermediate values, not just final output
  • Remove debug prints after fixing

Step 4: Fix

  • Change ONE thing at a time
  • If the same approach fails 3 times → completely different strategy
  • Don't add workarounds — fix the root cause
  • Common root causes:
    • Off-by-one errors (loop bounds, array indexing)
    • Type mismatches (int vs float, signed vs unsigned)
    • Encoding issues (UTF-8 vs bytes)
    • Path errors (relative vs absolute, missing trailing slash)
    • Race conditions (file not written yet, process not started)

Step 5: Verify

  • Run the same command that failed before
  • Test with multiple inputs, not just the one that was failing
  • Check edge cases: empty input, single element, very large input
  • Run any existing test suite

Read the full file on GitHub · 89 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 89 lines · 13 tokens per session scan A 1f65a08595e6

Subscribe to this mod's changes

systematic-debugging is a skill published in the GitHub repository vstorm-co/pydantic-deepagents (1,103 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 711 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.