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 ReSkin-Games/memorybank-plus --skill debug-agentgit clone --depth 1 https://github.com/ReSkin-Games/memorybank-plusWrote 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/reskin-games/memorybank-plus/debug-agent)<a href="https://agentmods.dev/skills/reskin-games/memorybank-plus/debug-agent"><img src="https://agentmods.dev/badge/skills/reskin-games/memorybank-plus/debug-agent/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/reskin-games/memorybank-plus/debug-agent"><img src="https://agentmods.dev/badge/skills/reskin-games/memorybank-plus/debug-agent.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 | $0.00060 | $0.02388 |
| Opus 5.5 | $0.00024 | $0.00955 |
| Sonnet 5.5 | $0.00012 | $0.00478 |
| Haiku 4.5 | $0.00006 | $0.00239 |
Grade A, and why
debug-agent 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 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.
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.
This is a copy
88% identical to debug-agent — 28 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug Mode
You are now in DEBUG MODE. You must debug with runtime evidence.
Why this approach: Traditional AI agents jump to fixes claiming 100% confidence, but fail due to lacking runtime information. They guess based on code alone. You cannot and must NOT fix bugs this way — you need actual runtime data.
Your systematic workflow:
- Generate 3-5 precise hypotheses about WHY the bug occurs (be detailed, aim for MORE not fewer)
- Instrument code with logs (see Logging section) to test all hypotheses in parallel
- Ask user to reproduce the bug. Provide clear, numbered reproduction steps. Remind the user to restart any apps/services if instrumented files are cached or bundled. Ask the user to confirm when done.
- Analyze logs: evaluate each hypothesis (CONFIRMED/REJECTED/INCONCLUSIVE) with cited log line evidence
- Fix only with 100% confidence and log proof; do NOT remove instrumentation yet
- Verify with logs: ask user to run again, compare before/after logs with cited entries
- If logs prove success and user confirms: remove logs and explain. If failed: FIRST remove any code changes from rejected hypotheses (keep only instrumentation and proven fixes), THEN generate NEW hypotheses from different subsystems and add more instrumentation
- After confirmed success: explain the problem and provide a concise summary of the fix (1-2 lines)
Critical constraints:
- NEVER fix without runtime evidence first
- ALWAYS rely on runtime information + code (never code alone)
- Do NOT remove instrumentation before post-fix verification logs prove success and user confirms that there are no more issues
- Fixes often fail; iteration is expected and preferred. Taking longer with more data yields better, more precise fixes
Logging
STEP 0: Start the logging server (MANDATORY BEFORE ANY INSTRUMENTATION)
CRITICAL: The server is a long-running process. You MUST run it in the BACKGROUND.
Run the debug server as a background process before any instrumentation. The server stays running for the entire debug session — it is NOT a one-shot command.
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 · 174 lines · 60 tokens per session scan A cc8d357adb55
debug-agent is a skill published in the GitHub repository ReSkin-Games/memorybank-plus (7 stars, last pushed 5mo ago), licensed MIT. It adds 60 tokens to every session and 2,388 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to debug-agent, differing in 28 lines, and is treated as a copy.
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