Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/planexeorg/planexe/run-napkin-math-pipeline)<a href="https://agentmods.dev/skills/planexeorg/planexe/run-napkin-math-pipeline"><img src="https://agentmods.dev/badge/skills/planexeorg/planexe/run-napkin-math-pipeline/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/planexeorg/planexe/run-napkin-math-pipeline"><img src="https://agentmods.dev/badge/skills/planexeorg/planexe/run-napkin-math-pipeline.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 | $0.00092 | $0.04461 |
| Opus 5 | $0.00046 | $0.02230 |
| Sonnet 5 | $0.00018 | $0.00892 |
| Haiku 4.5 | $0.00009 | $0.00446 |
Grade C, and why
run-napkin-math-pipeline scanned grade C 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 10d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
rm -rf $D/__pycache__ How it starts
The opening of the file, as written. The whole thing — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run the Napkin-Math Pipeline
Overview
End-to-end orchestrator for the pipeline documented in
experiments/napkin_math/README.md. Decides which stages to run based on
what is already present in the target output directory, then walks
forward through the remaining stages one at a time.
PlanExe source reports live under /Users/neoneye/git/PlanExe-web/
(e.g. /Users/neoneye/git/PlanExe-web/20251114_paperclip_automation/).
Outputs land under
experiments/napkin_math/output/<version>/<plan-slug>/.
What the orchestrator does and does not do
The orchestrator is dispatch-only. It looks at filenames in the target directory, decides which stage is missing, and delegates the work to the appropriate sibling skill (via a subagent) or Python runner. It does not read the content of large input files into its own context.
Do not read into orchestrator context:
extract_parameters_input.md(the digest — typically ~25K tokens)compress_*.mdorcompress_*_raw.jsonfiles- The raw PlanExe HTML report
- The full
parameters.json,bounds.json,scenarios.json,montecarlo.json(except quickjq/Python extraction of a single field)
OK to read into orchestrator context:
- Directory listings (filenames only)
validation.jsonexit status (via Python one-liner, not full file)montecarlo.jsonper-thresholdprobabilityvalues (via Python one-liner)- Sibling skill SKILL.md / system-prompt.txt only when updating the skill itself
Pulling a digest into orchestrator context defeats the architecture. Each stage's sibling skill knows how to read its own inputs; the orchestrator's job is to invoke that skill, not to do its work.
Per-stage delegation
For each missing stage, dispatch as follows. Pass the agent the file paths and a one-line task; do not paste file contents into the prompt.
| Stage | How to dispatch |
|---|---|
| 0. Digest | Bash → prepare_extract_input.py --planexe-dir <PlanExe-web/...> --output-dir <target> |
| 1. Parameters | Agent with the sibling skill name extract-parameters-from-digest; prompt: "Read <target>/extract_parameters_input.md per system-prompt.txt, write the result to <target>/parameters.json." |
| 2. Validation | Bash → validate_parameters.py --parameters … --output … |
| 3. Bounds | Agent with generate-bounds; prompt: "Read <target>/parameters.json per the generate-bounds rules, write <target>/bounds.json." |
| 4. Calculations | Agent with generate-calculations; prompt: "Read <target>/parameters.json, write <target>/calculations.py per the skill rules." |
| 5. Scenarios | Agent with run-scenarios; prompt: "Read parameters.json, bounds.json, calculations.py, write <target>/scenarios.json." |
| 6. MC settings | Small hand-written JSON file based on parameters.json — extract output_names via Python one-liner, write thresholds with >= 0. No content reading needed. |
| 7. Monte Carlo | Bash → run_monte_carlo.py … |
| 8. Assessment | Bash → summarize_assessment.py … |
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.
- 10d ago First seen · 341 lines · 92 tokens per session scan C 1cf2b28c6003
run-napkin-math-pipeline is a skill published in the GitHub repository PlanExeOrg/PlanExe (401 stars, last pushed 3d ago), licensed MIT. It adds 92 tokens to every session and 4,461 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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