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 bartoszarendt/agenticloop --skill debugging-before-fixesgit clone --depth 1 https://github.com/bartoszarendt/agenticloopWrote 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/bartoszarendt/agenticloop/debugging-before-fixes)<a href="https://agentmods.dev/skills/bartoszarendt/agenticloop/debugging-before-fixes"><img src="https://agentmods.dev/badge/skills/bartoszarendt/agenticloop/debugging-before-fixes/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/bartoszarendt/agenticloop/debugging-before-fixes"><img src="https://agentmods.dev/badge/skills/bartoszarendt/agenticloop/debugging-before-fixes.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 | $0.00077 | $0.01077 |
| Opus 5.5 | $0.00031 | $0.00431 |
| Sonnet 5.5 | $0.00015 | $0.00215 |
| Haiku 4.5 | $0.00008 | $0.00108 |
Grade A, and why
debugging-before-fixes 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.
3. A `curl` or API smoke call against the dev stack. How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debugging before fixes
No fix without a root cause first. Patching symptoms burns review iterations and often adds a second bug. Guess-and-check thrashing is how tasks end blocked or review-exhausted under [[blocked-state]].
Step 1: build the feedback loop
Before investigating or fixing, stand up a fast, deterministic, agent-runnable loop that reproduces the failure on demand. Reach for the highest item on this list that actually reproduces the failure:
- A failing unit, integration, or browser test.
- The targeted command from the task record's Required Checks.
- A
curlor API smoke call against the dev stack. - A small CLI or script run against a checked-in fixture.
- Replaying a captured payload, request, or log line through the failing code.
- A throwaway harness in
.agenticloop/tmp/that calls the failing unit directly. - A fuzz or property loop when the triggering input is unknown.
git bisector a differential loop when a previously passing behavior regressed.
Prefer the cheapest deterministic loop that still fails for the real reason. Capture the exact command: it becomes RED proof for [[tdd-implementation]] and evidence for [[verification-evidence]].
Phase 2: investigate
- Read the entire error message and stack trace.
- Run the Step 1 loop and watch the real failure.
- Diff what changed.
- Trace the bad value or state back to where it originates.
Phase 3: compare
Find code in this repo that does the same kind of thing and works. Read the reference fully, then list concrete differences between it and the broken path.
Phase 4: hypothesize and test
Calibrate effort to the failure:
- If the output fully explains a trivial error, fix it directly.
- If the failure is non-obvious, not fully explained, or already survived one fix attempt, write 3-5 ranked falsifiable hypotheses before changing code.
Put each hypothesis in this form:
If X is the cause, then observing or changing Y should produce Z.
Test hypotheses in rank order with the smallest observation or change that discriminates. Wrong hypotheses are reverted and recorded as ruled out.
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 Changed 39fc24bfd7cc
- 1mo ago First seen · 101 lines · 77 tokens per session scan A ffee49788dac
debugging-before-fixes is a skill published in the GitHub repository bartoszarendt/agenticloop (2 stars, last pushed 1mo ago), licensed MIT. It adds 77 tokens to every session and 1,077 once invoked, about $0.0003 per session on Opus 5.5. 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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