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/prabhdeepsingh/claude-plugins/debuggingnpx skills add PrabhdeepSingh/claude-plugins --skill debugginggit clone --depth 1 https://github.com/PrabhdeepSingh/claude-pluginsWhat 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 | $0.00100 | $0.02682 |
| Opus 5 | $0.00050 | $0.01341 |
| Sonnet 5 | $0.00020 | $0.00536 |
| Haiku 4.5 | $0.00010 | $0.00268 |
Grade A, and why
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 yesterday.
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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging — hypothesis testing, not patch roulette
Debugging is a science experiment, not a repair job. You form a theory of what's wrong, make the one change that theory predicts will alter the outcome, and observe. Most debugging time is wasted by skipping that discipline: patching where the error appeared instead of where it originated, changing three things at once, or trying fixes in a loop and keeping the wreckage of the ones that failed. The rules below are the difference between an hour and a day — and between a fix and a fix-shaped bug.
How to apply this
Run the loop in order: reproduce (for a production report, pull the real event first — section 2 feeds section 1) → read → locate → hypothesize → test the hypothesis → fix → prove. Don't skip ahead to "fix" — a fix you can't connect to a confirmed cause is a guess with good posture.
And stop the line. When something unexpected breaks mid-task, this loop preempts the feature work — don't push past a failing test or broken build to keep building. Errors compound: an unfixed bug in step 3 makes steps 4–6 wrong, and the wreckage multiplies the diagnosis.
And know what isn't a defect. A system that is slow but correct hasn't failed, and running this loop at it produces hypotheses no reproduction can settle — that is [[performance]], which borrows the one-change rule below and adds the baseline that decides whether a change was worth keeping.
1. Reproduce it first — no reproduction, no fix
You cannot verify a fix for something you cannot make happen. Before theorizing, make the failure occur on demand: the exact command, input, and state that triggers it. Then shrink it — smallest input, fewest steps — because every element you remove is a suspect eliminated.
If you can't reproduce it, that's not a dead end; reproducing it is now the task — and it has moves, bucketed by what varies. Timing-dependent? Add timestamps around the suspect area, widen the race window with artificial delays (sleep/setTimeout at the suspected interleaving point), run under load or concurrency to raise the collision probability. Environment-dependent? Diff runtime versions, env vars, and data state (an empty vs. populated database changes whole code paths); try reproducing in CI, where the environment is clean. State-dependent? Run the failing scenario in isolation versus after other operations — a difference means leaked state; hunt globals, singletons, shared caches (a failing test gets this first: run it alone to rule out test pollution). Truly random? Log defensively at the suspected site, alert on the exact error signature, write down the observed conditions, and revisit on recurrence — that's a plan, not a shrug. A "fix" shipped against an unreproduced bug is a coin flip you can't even watch land.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 104 lines · 100 tokens per session scan A 5d37760f02c2
debugging is a skill published in the GitHub repository PrabhdeepSingh/claude-plugins (3 stars, last pushed 2d ago), licensed MIT. It adds 100 tokens to every session and 2,682 once invoked, about $0.0005 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 2026-08-31.
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