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/planexeorg/planexe/summarize-assessmentnpx skills add PlanExeOrg/PlanExe --skill summarize-assessmentgit clone --depth 1 https://github.com/PlanExeOrg/PlanExeWrote 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/summarize-assessment)<a href="https://agentmods.dev/skills/planexeorg/planexe/summarize-assessment"><img src="https://agentmods.dev/badge/skills/planexeorg/planexe/summarize-assessment.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 | $0.00104 | $0.03328 |
| Opus 5 | $0.00052 | $0.01664 |
| Sonnet 5 | $0.00021 | $0.00666 |
| Haiku 4.5 | $0.00010 | $0.00333 |
Grade A, and why
summarize-assessment 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 5d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Summarize napkin_math outputs into a plan assessment
Overview
A thin wrapper around experiments/napkin_math/summarize_assessment.py. The script reads the pipeline artifacts and emits assessment.md next to them. The output is an interpretation layer: it tells the next reader of this directory what the simulation tested, which gates fail or pass, which inputs drive the result, which assumptions remain unvalidated, and what to inspect next. The raw distributions live in montecarlo.json; assessment.md references them via the provenance map rather than reproducing them.
When to Use
- The Monte Carlo stage has just produced
montecarlo.jsonand the user wants a verdict - The user asks "is this plan in trouble?" or "what does the simulation say?"
- After any iteration on bounds or calculations, to see how the gate signals moved
Not for: producing the simulation itself (monte-carlo), running scenarios (run-scenarios), or extracting parameters from a report.
Workflow
-
Locate the inputs. Required:
parameters.json. Optional but recommended:bounds.json,scenarios.json,montecarlo.json.validation.jsonandmontecarlo_settings.jsonare picked up automatically from the same directory if present. The script degrades gracefully if any optional file is missing — it just omits that section. Ifparameters.jsonis missing, ask. -
Invoke the script. Requires Python 3.11+ (no extra deps):
/opt/homebrew/bin/python3.11 experiments/napkin_math/summarize_assessment.py \ --parameters <path>/parameters.json \ --bounds <path>/bounds.json \ --scenarios <path>/scenarios.json \ --montecarlo <path>/montecarlo.json \ [--output <path>/assessment.md]Default output:
<dir-of-parameters>/assessment.md. The script prints the output path on stdout. -
Report back. Tell the user the output path. If the user asks for a verdict in-conversation, read
assessment.mdand quote the gate-verdict rows and critical-findings bullets verbatim — don't paraphrase.
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.
- 5d ago First seen · 125 lines · 104 tokens per session scan A b51afd3fd151
summarize-assessment is a skill published in the GitHub repository PlanExeOrg/PlanExe (398 stars, last pushed 5d ago), licensed MIT. It adds 104 tokens to every session and 3,328 once invoked, about $0.0005 per session on Opus 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 2026-08-30.
Other skills, from other repositories
session-rag-eval
Run and debug Chatbox session attachment RAG model evaluation with synthetic and real long-file fixtures.
designer_guide
Guidance for creating and editing designs using the designer tool.
lynkr
Universal LLM gateway with intelligent routing, Graphify code intelligence, Distill compression, routing telemetry, Code Mode, and 12+ provider support. 60-80% cost reduction for Claude Code, Cursor, and Codex.
agents_guide
Guidance for delegating focused work to child Agents.
文档协作
引导用户通过结构化的文档共同编写工作流程。当用户想撰写文档、提案、技术规范、决策文档或类似结构化内容时使用。该工作流程帮助用户高效传递上下文，通过迭代优化内容，并验证文档对读者有效。当用户提到写文档、创建提案、起草规范或类似文档任务时触发。.
data_analyst
Analyses datasets with professional rigour — statistical summaries, clear narratives, and well-chosen visualisations.