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/Edison-Watch/Custom-MCPsWrote 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/edison-watch/custom-mcps/long_run_code)<a href="https://agentmods.dev/rules/edison-watch/custom-mcps/long_run_code"><img src="https://agentmods.dev/badge/rules/edison-watch/custom-mcps/long_run_code/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/edison-watch/custom-mcps/long_run_code"><img src="https://agentmods.dev/badge/rules/edison-watch/custom-mcps/long_run_code.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 | $0.00000 | $0.00222 |
| Opus 5.5 | $0.00000 | $0.00089 |
| Sonnet 5.5 | $0.00000 | $0.00044 |
| Haiku 4.5 | $0.00000 | $0.00022 |
Grade A, and why
long_run_code 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
100% identical to long_run_code — 0 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.
What it actually says
This pattern should apply for all long-running code.
Unbundle run() into:
- init()
- continue(id)
- cleanup(id)
Never assume you will call these in order.
Always checkpoint and resume from id's.
Pass nothing else.
This forces you to keep things serializable and therefore loggable, reproducible, parallelizable.
by the way try not to name them 'id' if you can add extra detail like 'runId', 'taskId', 'subTask2Id'.
Any time you cross a system boundary*:
RATE LIMITS, TIMEOUTS, RETRIES, LOG TRACE.
NO EXCEPTIONS.
*eg call microservice or external api. yes, this includes llm APIs, meaning every ai project needs this
DO NOT DO ANY OUTPUT FORMATTING UNTIL THE END
"its so easy to fstring/template string"
DOESNT MEAN YOU SHOULD
keep things in structured format all the way basically until the point of reading
debugging output formatting its own form of hell
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 · 40 lines · 0 tokens per session scan A 2b527640b638
long_run_code is a cursor rule published in the GitHub repository Edison-Watch/Custom-MCPs (0 stars, last pushed 18d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 222 tokens. A static security scan graded it A with 0 findings. It is 100% identical to long_run_code, differing in 0 lines, and is treated as a copy.
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dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
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config-validation-and-normalization
Config validation and normalization responsibilities (defineConfig normalizes, the orm section schema validates and resolves paths, the loader reports per-section diagnostics).