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/bartoszarendt/agenticloop/debugging-before-fixesnpx skills add bartoszarendt/agenticloop --skill debugging-before-fixesgit clone --depth 1 https://github.com/bartoszarendt/agenticloopWhat 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 | $0.00077 | $0.01076 |
| Opus 5 | $0.00039 | $0.00538 |
| Sonnet 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 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.
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.
- 2d 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 12d ago), licensed MIT. It adds 77 tokens to every session and 1,076 once invoked, about $0.0004 per session on Opus 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.
Other skills, from other repositories
consulting-analysis
Use this skill when the user requests to generate, create, or write professional research reports including but not limited to market analysis, consumer insights, brand analysis, financial analysis, industry research, competitive intelligence, investment due diligence, or any consulting-grade analytical report. This…
skill-creator
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
ppt-generation
Use this skill when the user requests to generate, create, or make presentations (PPT/PPTX). Creates visually rich slides by generating images for each slide and composing them into a PowerPoint file.
deerflow-maintainer-orchestrator
Use when a DeerFlow maintainer needs comment-only GitHub issue or PR handling: resolve issue/PR scopes with gh, analyze issues, post or draft issue comments, perform PR review comments, review PR or issue batches, compare competing PRs that target the same issue, give fix strategy, risk classification, and validation…
systematic-literature-review
Use this skill when the user wants a systematic literature review, survey, or synthesis across multiple academic papers on a topic. Also covers annotated bibliographies and cross-paper comparisons. Searches arXiv and outputs reports in APA, IEEE, or BibTeX format. Not for single-paper tasks — use academic-paper-review…
code-documentation
Use this skill when the user requests to generate, create, or improve documentation for code, APIs, libraries, repositories, or software projects. Supports README generation, API reference documentation, inline code comments, architecture documentation, changelog generation, and developer guides. Trigger on requests…