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.
npx skills add sammcj/agentic-coding --skill systematic-debugginggit clone --depth 1 https://github.com/sammcj/agentic-codingWrote 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.
[](https://agentmods.dev/skills/sammcj/agentic-coding/systematic-debugging)<a href="https://agentmods.dev/skills/sammcj/agentic-coding/systematic-debugging"><img src="https://agentmods.dev/badge/skills/sammcj/agentic-coding/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.
<a href="https://agentmods.dev/skills/sammcj/agentic-coding/systematic-debugging"><img src="https://agentmods.dev/badge/skills/sammcj/agentic-coding/systematic-debugging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 74 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.01037 |
| Opus 5.5 | $0.00022 | $0.00415 |
| Sonnet 5.5 | $0.00011 | $0.00207 |
| Haiku 4.5 | $0.00006 | $0.00104 |
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 14d 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.
How it starts
The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Debugging with Fagan Inspection
This skill applies a modified Fagan Inspection methodology for systematic problem resolution when facing complex problems or stubborn bugs that have resisted multiple fix attempts.
Process Overview
Create tasks for each of the four phases, then work them to completion, marking each done before starting the next.
Follow these four phases sequentially. Do not skip phases or attempt fixes before completing the inspection.
Phase 1: Initial Overview
Establish a clear understanding of the problem before analysis:
- Explain the problem in plain language without technical jargon
- State expected behaviour - what should happen
- State actual behaviour - what is happening instead
- Document symptoms - error messages, logs, observable failures
- Context - when does it occur, how often, under what conditions
- Prior fix attempts - list each attempt, what it changed, and why it failed; treat these as eliminated hypotheses that Phase 3 must account for
Output: A clear problem statement that anyone could understand.
Phase 2: Systematic Inspection
Perform a line-by-line walkthrough as the "Reader" role in Fagan Inspection. Identify defects without attempting to fix them yet - this is pure inspection.
Check against these defect categories:
- Logic Errors
- Boundary Conditions
- Error Handling
- Data Flow Issues
- Integration Points
Talk through the code step by step, as the Reader does in a Fagan inspection. For each section of code:
- State what the code is intended to do
- Identify any discrepancies between intent and implementation
- Flag assumptions or unclear aspects
- Use ultrathink to think deeper on complex sections
Output: A categorised list of identified defects with line numbers and specific descriptions.
Phase 3: Root Cause Analysis
After identifying issues, trace back to find the fundamental cause - not just symptoms.
Five Whys Technique:
- Ask "why" repeatedly (at least 3-5 times) to get to the underlying issue
- State each "why" explicitly in your analysis
- Example:
- Why did the API call fail? → Because the request was malformed
- Why was it malformed? → Because the data wasn't serialised correctly
- Why wasn't it serialised? → Because the serialiser expected a different type
- Why did it expect a different type? → Because the schema was updated but code wasn't
- Root cause: Schema versioning mismatch between services
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.
- 14d ago Changed · -17 tokens per session 4c960dc995d2
- 1mo ago First seen · 114 lines · 72 tokens per session scan A 68aff372c459
systematic-debugging is a skill published in the GitHub repository sammcj/agentic-coding (165 stars, last pushed today), licensed Apache-2.0. It adds 55 tokens to every session and 1,037 once invoked, about $0.0002 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.
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