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 ronmkr/PromptBook --skill interactive-bug-reportinggit clone --depth 1 https://github.com/ronmkr/PromptBookWrote 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/ronmkr/promptbook/interactive-bug-reporting)<a href="https://agentmods.dev/skills/ronmkr/promptbook/interactive-bug-reporting"><img src="https://agentmods.dev/badge/skills/ronmkr/promptbook/interactive-bug-reporting/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/ronmkr/promptbook/interactive-bug-reporting"><img src="https://agentmods.dev/badge/skills/ronmkr/promptbook/interactive-bug-reporting.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 | $0.00062 | $0.01067 |
| Opus 5.5 | $0.00025 | $0.00427 |
| Sonnet 5.5 | $0.00012 | $0.00213 |
| Haiku 4.5 | $0.00006 | $0.00107 |
Grade A, and why
interactive-bug-reporting 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.
This is a copy
97% identical to qa — 2 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QA Session
Run an interactive QA session. The user describes problems they're encountering. You clarify, explore the codebase for context, and file GitHub issues that are durable, user-focused, and use the project's domain language.
For each issue the user raises
1. Listen and lightly clarify
Let the user describe the problem in their own words. Ask at most 2-3 short clarifying questions focused on:
- What they expected vs what actually happened
- Steps to reproduce (if not obvious)
- Whether it's consistent or intermittent
Do NOT over-interview. If the description is clear enough to file, move on.
2. Explore the codebase in the background
While talking to the user, kick off an Agent (subagent_type=Explore) in the background to understand the relevant area. The goal is NOT to find a fix — it's to:
- Learn the domain language used in that area (check UBIQUITOUS_LANGUAGE.md)
- Understand what the feature is supposed to do
- Identify the user-facing behavior boundary
This context helps you write a better issue — but the issue itself should NOT reference specific files, line numbers, or internal implementation details.
3. Assess scope: single issue or breakdown?
Before filing, decide whether this is a single issue or needs to be broken down into multiple issues.
Break down when:
- The fix spans multiple independent areas (e.g. "the form validation is wrong AND the success message is missing AND the redirect is broken")
- There are clearly separable concerns that different people could work on in parallel
- The user describes something that has multiple distinct failure modes or symptoms
Keep as a single issue when:
- It's one behavior that's wrong in one place
- The symptoms are all caused by the same root behavior
4. File the GitHub issue(s)
Create issues with gh issue create. Do NOT ask the user to review first — just file and share URLs.
Issues must be durable — they should still make sense after major refactors. Write from the user's perspective.
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 · 131 lines · 62 tokens per session scan A de8120b195dd
interactive-bug-reporting is a skill published in the GitHub repository ronmkr/PromptBook (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 62 tokens to every session and 1,067 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to qa, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
diagnose
Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.
zoom-out
Tell the agent to zoom out and give broader context or a higher-level perspective. Use when you're unfamiliar with a section of code or need to understand how it fits into the bigger picture.
optimization
Use when improving performance, latency, throughput, memory usage, or general efficiency. Start by defining target metrics, measuring comprehensively, attributing bottlenecks, validating with static analysis, and prioritizing macro-optimizations before micro-optimizations.
ix
Answer structural questions about a codebase — what a symbol is, what calls it, what a change breaks, where the hotspots are — from a persistent code graph via the ix CLI, instead of grepping.
triage
Diagnose and fix broken MCP servers in mcptoon — doctor, health, per-server probes, and the common failure playbook.
arkcli-doctor
A diagnostic tool for ARK services, accounts, models, and inference endpoints. An inference endpoint is the online address through which an AI model receives requests.