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/helderberto/agent-skills/diagnosenpx skills add helderberto/agent-skills --skill diagnosegit clone --depth 1 https://github.com/helderberto/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/helderberto/agent-skills/diagnose)<a href="https://agentmods.dev/skills/helderberto/agent-skills/diagnose"><img src="https://agentmods.dev/badge/skills/helderberto/agent-skills/diagnose.svg" alt="Measured on agentmods" 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 | $0.00098 | $0.02108 |
| Opus 5 | $0.00049 | $0.01054 |
| Sonnet 5 | $0.00020 | $0.00422 |
| Haiku 4.5 | $0.00010 | $0.00211 |
Grade A, and why
diagnose 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.
Phase 1 is done when you can name **one command** (script path, test invocation, curl) that you have **already run at least once** — show the invocation and its output, redacted — and that is: This is a copy
80% identical to diagnosing-bugs — 79 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnose
A discipline for hard bugs. Skip phases only when explicitly justified.
When exploring the codebase, build a clear mental model of the relevant modules and check any ADRs or design notes in the area you're touching.
Redact
This skill has you show commands, outputs, and captured artifacts. Redact every secret first: write <REDACTED> in its place. Build loops against env vars so credentials stay in the environment, not in what you show. Captured artifacts (HAR files, log dumps) carry auth headers — quote only the lines that carry signal. If redacted output isn't enough to diagnose, say so and ask the user.
Phase 1 — Build a feedback loop
This is the skill. Everything else is mechanical. If you have a fast, deterministic, agent-runnable pass/fail signal for the bug, you will find the cause — bisection, hypothesis-testing, and instrumentation all just consume that signal. If you don't have one, no amount of staring at code will save you.
Spend disproportionate effort here. Be aggressive. Be creative. Refuse to give up.
Ways to construct one — try them in roughly this order
- Failing test at whatever seam reaches the bug — unit, integration, e2e.
- Curl / HTTP script against a running dev server.
- CLI invocation with a fixture input, diffing stdout against a known-good snapshot.
- Headless browser script (Playwright / Puppeteer) — drives the UI, asserts on DOM/console/network.
- Replay a captured trace. Save a real network request / payload / event log to disk; replay it through the code path in isolation.
- Throwaway harness. Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.
- Property / fuzz loop. If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
- Bisection harness. If the bug appeared between two known states (commit, dataset, version), automate "boot at state X, check, repeat" so you can
git bisect runit. - Differential loop. Run the same input through old-version vs new-version (or two configs) and diff outputs.
- HITL loop. Last resort. If a human must click, drive them with a structured script — show one instruction, wait for Enter, capture the answer (y/n, pasted error text) back to you — so the loop stays disciplined instead of ad-hoc.
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 Changed · +32 tokens per session 6e347c43014a
- 6d ago First seen · 136 lines · 66 tokens per session scan A 34885f8e3b97
diagnose is a skill published in the GitHub repository helderberto/agent-skills (14 stars, last pushed 4d ago), licensed MIT. It adds 98 tokens to every session and 2,108 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 80% identical to diagnosing-bugs, differing in 79 lines, and is treated as a copy.
Other skills, from other repositories
debug-systematic
Systematic 4-phase debugging methodology for complex, intermittent, or mysterious issues. Use when investigating bugs, race conditions, or unexplained failures.
observability
OpenTelemetry, distributed tracing, structured logging, metrics (Prometheus, Grafana, Datadog). Use when implementing monitoring, tracing, or debugging production issues.
debug-methodical
Debugging méthodique en 4 phases (reproduce → isolate → fix → verify). Use when investigating a bug, regression, flaky test, or unexpected behavior.
kiss-dry-yagni
Principes KISS, DRY, YAGNI. Use when reviewing code quality or refactoring.
performance
Performance & Optimisation - Atoll Tourisme. Use when optimizing performance or profiling code.
audit-dead-code
Hunt dead code across a whole repository through four labelled lanes of unequal confidence. Knip (TS/JS unused files, exports, types, enum members), vulture (Python symbols), gopls (Go unexported symbols), and a portable grep lane (shell and other symbol languages), then adjudicate every candidate against the…