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 agentmods add skills/bostonaholic/team/systematic-debuggingnpx skills add bostonaholic/team --skill systematic-debugginggit clone --depth 1 https://github.com/bostonaholic/teamWhat 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 | $0.00027 | $0.01647 |
| Opus 5 | $0.00014 | $0.00823 |
| Sonnet 5 | $0.00005 | $0.00329 |
| Haiku 4.5 | $0.00003 | $0.00165 |
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 2d 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Debugging
Never skip to fixing. Understand the cause first. A fix applied without understanding the root cause is a coin flip — it may mask the symptom while leaving the disease.
4-Phase Investigation
Follow
skills/progress-tracking/SKILL.md: when this procedure has two or more steps, seed one todo item per step before starting and mark each complete as you go.
Phase 1: OBSERVE
Gather evidence before forming any theories. The goal is to build a factual picture of what is happening.
- Read error messages completely. The first line is the symptom. The stack trace is the geography. The last frame before your code is where to look.
- Reproduce the failure. If you cannot reproduce it, you cannot verify your fix. Document the exact reproduction steps.
- Collect multiple data points. One error message is an anecdote. Three error messages are a pattern. Gather logs, stack traces, test output, and runtime state.
- Note what IS working. The boundary between working and broken code narrows the search space dramatically.
- Record timestamps and sequence. When did it start failing? What changed just before? Check git log, deployment history, and dependency updates.
- Treat intermittency as evidence, not noise. A test that fails 1 in 10
runs is not "flaky". It reports a real condition that most invocations do
not hit: timing, ordering, resource contention, or hidden global state. The
conditions that make a test intermittent are frequently the conditions that
make the product intermittently misbehave in production. Record the failure
rate (e.g., 3/30 runs), the variance across environments (local vs CI), what
is concurrent/asynchronous/stateful in the path, and any shared state
(
/tmp, env vars, singletons, DB rows).
Do not hypothesize during OBSERVE. Just collect.
Phase 2: HYPOTHESIZE
Form theories that explain ALL the observed evidence. A hypothesis that explains only some observations is incomplete.
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.
- 2d ago First seen · 153 lines · 27 tokens per session scan A 0e6453bf0b67
systematic-debugging is a skill published in the GitHub repository bostonaholic/team (11 stars, last pushed 2d ago), licensed MIT. It adds 27 tokens to every session and 1,647 once invoked, about $0.0001 per session on Opus 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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