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 markoblogo/abvx-agent-skills --skill diagnosegit clone --depth 1 https://github.com/markoblogo/abvx-agent-skillsWrote 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/markoblogo/abvx-agent-skills/diagnose)<a href="https://agentmods.dev/skills/markoblogo/abvx-agent-skills/diagnose"><img src="https://agentmods.dev/badge/skills/markoblogo/abvx-agent-skills/diagnose/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/markoblogo/abvx-agent-skills/diagnose"><img src="https://agentmods.dev/badge/skills/markoblogo/abvx-agent-skills/diagnose.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00056 | $0.00519 |
| Opus 5 | $0.00028 | $0.00260 |
| Sonnet 5 | $0.00011 | $0.00104 |
| Haiku 4.5 | $0.00006 | $0.00052 |
Grade A, and why
diagnose 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 9d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnose
The core job is to build a reliable feedback loop. Without one, hypotheses are weak.
Phase 1: Feedback Loop
Create the smallest useful pass/fail signal:
- failing test;
- CLI command with fixture;
- HTTP request against local server;
- browser script;
- replayed trace or payload;
- small harness around the relevant function;
- repeated flake trigger with counted failure rate.
If no loop can be built, state what was tried and ask for logs, traces, access, or permission to instrument.
Phase 2: Reproduce
Confirm the loop matches the user's symptom, not a nearby failure. Capture exact error text, wrong output, timing, network failure, or UI state.
For nondeterministic issues, raise the reproduction rate with repetition, stress, seeded randomness, timing controls, and isolation.
Phase 3: Hypothesize
Write 3-5 ranked hypotheses before editing. Each must include:
Hypothesis:
Prediction:
Probe:
Result:
Status:
Test one variable at a time. Update rankings as evidence arrives.
Phase 4: Instrument
- Prefer debugger, REPL, targeted logs, traces, or profiler over broad logging.
- Tag temporary instrumentation with a unique marker such as
[DEBUG-7b3c]. - Remove all temporary instrumentation before finalizing.
- For performance regressions, measure first and fix second.
Phase 5: Fix And Regression Test
If a correct seam exists, turn the minimized repro into a failing regression check before the fix. If no correct seam exists, document that as an architecture gap.
Apply the smallest fix supported by evidence, then rerun:
- the regression check;
- the original feedback loop;
- relevant project checks.
When the task needs an auditable incident, device, CI/runtime, or regression record, hand the established reproducer and verification commands to bug-evidence-protocol. That protocol records proof; it does not replace this diagnostic loop.
Final Report
Include root cause, winning hypothesis, files changed, verification, debug cleanup, and residual risk.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 71 lines · 56 tokens per session scan A 8ade5652175f
diagnose is a skill published in the GitHub repository markoblogo/abvx-agent-skills (16 stars, last pushed today), licensed MIT. It adds 56 tokens to every session and 519 once invoked, about $0.0003 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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