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 yuri-semenenko/ai-engineering-workspace --skill debuggit clone --depth 1 https://github.com/yuri-semenenko/ai-engineering-workspaceWrote 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/yuri-semenenko/ai-engineering-workspace/debug)<a href="https://agentmods.dev/skills/yuri-semenenko/ai-engineering-workspace/debug"><img src="https://agentmods.dev/badge/skills/yuri-semenenko/ai-engineering-workspace/debug/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/yuri-semenenko/ai-engineering-workspace/debug"><img src="https://agentmods.dev/badge/skills/yuri-semenenko/ai-engineering-workspace/debug.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.01863 |
| Opus 5.5 | $0.00000 | $0.00745 |
| Sonnet 5.5 | $0.00000 | $0.00373 |
| Haiku 4.5 | $0.00000 | $0.00186 |
Grade A, and why
debug scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. **Reproduce and build a feedback loop.** Get a deterministic, minimal reproduction first — exact inputs, environment, failing path. Then turn it into the cheapest signal you can rerun on demand: the rate of feedback i How it starts
The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug
Drive a broken system to its root cause with explicit, falsifiable reasoning, then fix the cause rather than the symptom. Optimized for a Staff Engineer who values root-cause over patching, minimal change, and the rule: if you can't explain why the fix works, you haven't found the bug yet.
Do not jump to a fix. The job is to understand first. A fix that makes the symptom disappear without a confirmed cause is a deferred bug, not a resolved one.
Phases
Work through these in order and state which phase you're in. Don't skip ahead — most bad fixes come from hypothesizing before observing, or fixing before reproducing.
- Reproduce and build a feedback loop. Get a deterministic, minimal reproduction first — exact inputs, environment, failing path. Then turn it into the cheapest signal you can rerun on demand: the rate of feedback is the speed limit on the whole investigation. Take the first rung that fits — a failing unit/integration test, an HTTP probe (
curl), a one-shot CLI snapshot, a headless-browser script, a replay of a captured trace or log, a throwaway harness,git bisect, a property/fuzz case — then tighten it until it is faster, sharper (fails for one reason), and deterministic. Gate before moving on: name one command you have already run that is red-capable, deterministic, fast, and agent-runnable. No such command means no reliable signal — say so and treat any fix as unconfirmed. - Observe. Read the actual evidence — error message, stack trace, logs, failing assertion — before theorizing. Separate what you know (observed) from what you assume. Quote the real error; do not paraphrase from memory.
- Isolate and minimize the case. Shrink to the smallest input and shortest code path that still triggers the bug. Binary-search the code path or input (
git bisect, disabling halves, narrowing the dataset, deleting unrelated setup), add tracing at boundaries. A minimal reproducer is often the diagnosis: each thing you remove without the bug disappearing is a thing that was not the cause. - Hypothesize. Form 1-3 explicit, falsifiable hypotheses ranked by likelihood. Each must predict something observable and state what would disprove it. Distinguish the proximate cause (the line that threw) from the root cause (why the bad state existed at all).
- Test the hypothesis. Run the cheapest disproving experiment first. Confirm the cause before touching the fix. Change one variable at a time.
- Fix at the root. Minimal change that addresses the confirmed cause. Resist masking — a swallowed error, a defensive null-check that hides why the value was null, a retry wrapped around a logic bug. Do not refactor while debugging; that is a separate change.
- Verify genuinely. The original reproduction now passes, the fix matches the confirmed hypothesis, and a regression test fails without the fix. Check you didn't just move the bug or break an adjacent path. Remove any temporary instrumentation.
- Prevent (if systemic). Ask whether a type, invariant, test, or lint rule would have caught this class of bug. Surface it; don't force it.
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.
- 1mo ago First seen · 76 lines · 0 tokens per session scan A 28874e5975d4
debug is a skill published in the GitHub repository yuri-semenenko/ai-engineering-workspace (4 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,863 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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