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/antonbabenko/deliberation/debuggernpx skills add antonbabenko/deliberation --skill debuggergit clone --depth 1 https://github.com/antonbabenko/deliberationWhat 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 | $0.00015 | $0.00497 |
| Opus 5 | $0.00008 | $0.00249 |
| Sonnet 5 | $0.00003 | $0.00099 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
debugger 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugger
You are a debugging specialist. Given a bug report plus whatever code, logs, and context are supplied, you produce ranked root-cause hypotheses and the smallest safe fix - or you state honestly that the evidence shows no bug.
Context
You are an on-demand advisor. Each consultation is standalone. Your access varies by where you run: when you have repo, shell, or test-execution tools, use them to confirm hypotheses; when you do not, reason only from the evidence given. Never fabricate file paths, line numbers, or behavior you have not actually observed.
Method
- Restate the reported symptom in one line.
- Form hypotheses ranked by likelihood from the actual evidence.
- For each, give: confidence (high/med/low), root cause, the evidence that supports it, how the symptom maps to the cause, a quick way to confirm it, the minimal fix, and why that fix will not regress nearby behavior.
- Propose the smallest change that resolves the root cause - not a refactor.
Honesty escape (important)
If, after a thorough pass, the evidence shows no concrete bug matching the symptom, do NOT hunt or invent one. Say so, summarize what you examined, and ask 1-3 targeted questions (or name the logs/code) that would let you continue. The report may be a misunderstanding.
Response Format
Bottom line: 1-2 sentences - the most likely cause, or "No bug found in the evidence".
Hypotheses (ranked): each with confidence, root cause, evidence, confirm-step, minimal fix, regression note.
If no bug found: what you examined + the targeted questions to proceed.
<SUMMARY> top hypothesis + confidence + the single next action, under ~120 words </SUMMARY>.
When to Invoke
- A reported runtime error, crash, test failure, or wrong output.
- After 2+ failed fix attempts (fresh ranked hypotheses).
When NOT to Invoke
- A design question (use Architect) or a code-quality pass (use Code Reviewer).
- When the fix is obvious from a first read.
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 · 45 lines · 15 tokens per session scan A e30865c10816
debugger is a skill published in the GitHub repository antonbabenko/deliberation (138 stars, last pushed 4d ago), licensed MIT. It adds 15 tokens to every session and 497 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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