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.
git clone --depth 1 https://github.com/Oriolshhh/runware-image-mcpWrote 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/rules/oriolshhh/runware-image-mcp/debugger)<a href="https://agentmods.dev/rules/oriolshhh/runware-image-mcp/debugger"><img src="https://agentmods.dev/badge/rules/oriolshhh/runware-image-mcp/debugger/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/rules/oriolshhh/runware-image-mcp/debugger"><img src="https://agentmods.dev/badge/rules/oriolshhh/runware-image-mcp/debugger.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 | $0.00018 | $0.00810 |
| Opus 5.5 | $0.00007 | $0.00324 |
| Sonnet 5.5 | $0.00004 | $0.00162 |
| Haiku 4.5 | $0.00002 | $0.00081 |
Grade A, and why
debugger 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.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role: debugger
Debugger
Purpose
Turn an observed failure into a verified root cause and the smallest safe fix, without guessing or masking the symptom.
Responsibilities
- Establish the exact expected and observed behavior.
- Reuse a supplied context capsule or fresh
.agent/context/before broad search. - Produce the smallest deterministic reproduction available.
- Maintain an evidence ledger separating observations, hypotheses, experiments, and conclusions.
- Test one falsifiable hypothesis at a time and rank alternatives.
- Add a regression test that fails for the root cause, not merely the symptom.
- Apply the smallest fix only after evidence identifies the causal mechanism.
When to invoke it
- A bug, crash, failing/flaky test, regression, race, incorrect result, or unexplained performance degradation needs diagnosis and correction.
- A previous attempted fix failed or the symptom spans module boundaries.
Required inputs
- Symptom, expected behavior, reproduction steps or failing command, relevant logs/errors, environment, and known recent changes when available.
Operating instructions
- Read the context capsule first; otherwise apply
context-discoveryand build a task-specific capsule. Do not rescan architecture already summarized. - Reproduce before editing. Record the exact command/input, environment, and complete relevant failure. If reproduction is impossible, stop and name what evidence is missing.
- Reduce the reproduction and identify the last known-good boundary when possible. Trace data/control flow through cited source paths.
- List ranked falsifiable hypotheses. For each experiment, state what result would support or reject the hypothesis before running it.
- Change one variable at a time. Do not stack speculative fixes.
- When the root cause is supported, add or strengthen a regression test, apply the minimal fix, and remove temporary instrumentation unless requested.
- Re-run the reproduction, focused regression tests, relevant wider tests, and configured gates. Check adjacent failure paths.
- Stop on success or when
debug-loop.max_iterationsis reached. At the limit, return the evidence ledger and next discriminating experiment; do not continue.
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 · 84 lines · 18 tokens per session scan A 20633b05a679
debugger is a cursor rule published in the GitHub repository Oriolshhh/runware-image-mcp (0 stars, last pushed 3mo ago), licensed MIT. It adds 18 tokens to every session and 810 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
Other cursor rules, from other repositories
e2e-testing
E2E Flow Tests setup and debugging guide.
common-fixes
Utility for VAN QA common validation fixes.
hatch3r-dynamic-stack-verification
Runtime-evidence gates for stacks with no static type layer (plain-JS Vue, dynamic persistence) — smoke-mount changed components, dormant-collection-read ratchet, runtime smoke evidence on module-boundary changes; a green lint+build+mock-suite proves a layer, not the system.
roslyn-mcp-pitfalls
Known pitfalls and regression lessons from production use.
debug-issue
When the user reports a bug, an error, or unexpected behavior. Enforces four structured phases — reproduction, failing test, root cause isolation, fix and verify — to stop guess-and-check loops.
系统化调试
A step-by-step method for investigating bugs, failed tests, and unexpected behaviour. It provides a structured way to narrow down the cause instead of guessing.