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 soleimanmansouri/debug-bank --skill auto-instrumentgit clone --depth 1 https://github.com/soleimanmansouri/debug-bankWrote 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/soleimanmansouri/debug-bank/auto-instrument)<a href="https://agentmods.dev/skills/soleimanmansouri/debug-bank/auto-instrument"><img src="https://agentmods.dev/badge/skills/soleimanmansouri/debug-bank/auto-instrument/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/soleimanmansouri/debug-bank/auto-instrument"><img src="https://agentmods.dev/badge/skills/soleimanmansouri/debug-bank/auto-instrument.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 | $0.00019 | $0.00808 |
| Opus 5.5 | $0.00008 | $0.00323 |
| Sonnet 5.5 | $0.00004 | $0.00162 |
| Haiku 4.5 | $0.00002 | $0.00081 |
Grade A, and why
auto-instrument scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto-Instrument Skill
Use when the symptom classifier returns no match and the domain catalog is empty. Do NOT skip Step 0.
Step 0: Confirm No Match
Verify the classifier was already run. Expected output: "No pattern match" or all checklist scores 0/3. If you haven't run the classifier yet, do that first.
Step 1: Generate Hypotheses
H1: [cause] — Test: [what log value would confirm this]
H2: [cause] — Test: [what log value would confirm this]
H3: [cause] — Test: [what log value would confirm this]
Rank by proximity to the error site. Max 5 hypotheses.
Step 2: Instrument
Ensure the log directory exists: mkdir -p .debug-bank
Python
# region DEBUG-H1
import logging as _dbg; _dbg.basicConfig(filename='.debug-bank/debug.log', level=_dbg.DEBUG)
_dbg.debug(f"[DEBUG H1] var={var!r}")
# endregion DEBUG-H1
JavaScript / TypeScript
// #region DEBUG-H1
const _fs = require('fs'); _fs.appendFileSync('.debug-bank/debug.log', `[DEBUG H1] var=${JSON.stringify(var)}\n`);
// #endregion DEBUG-H1
Go
// #region DEBUG-H1
_f, _ := os.OpenFile(".debug-bank/debug.log", os.O_APPEND|os.O_CREATE|os.O_WRONLY, 0644)
fmt.Fprintf(_f, "[DEBUG H1] var=%v\n", var)
_f.Close()
// #endregion DEBUG-H1
Rules: log ONLY to .debug-bank/debug.log, never stdout. Never modify business logic.
Step 3: Reproduce
Run the failing operation. Logs capture evidence per hypothesis.
Step 4: Analyze
Read .debug-bank/debug.log. For each hypothesis:
H1: CONFIRMED / REJECTED / INCONCLUSIVE — [evidence]
Step 5: Fix
Minimal fix for the confirmed hypothesis only. Verify it resolves the symptom.
Step 6: Extract Pattern Candidate
candidate_id: P-candidate-YYYY-MM-DD
name: [from root cause]
category: [config / data / async / auth / type / state / network]
symptom_keywords: [from symptom]
checklist:
- [confirmed hypothesis — what to check first]
- [log expression that revealed it]
- [signal that ruled out other hypotheses]
debugger_strategy:
breakpoints: [file:function where bug was found]
watch_expressions: [log expression that showed the evidence]
fix_summary: [one sentence]
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 · 99 lines · 0 tokens per session scan A 3abf76468126
auto-instrument is a skill published in the GitHub repository soleimanmansouri/debug-bank (1 stars, last pushed 5mo ago), licensed MIT. It adds 19 tokens to every session and 808 once invoked, about $0.0001 per session on Opus 5.5. 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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