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/extract-parameters-from-fullnpx skills add PlanExeOrg/PlanExe --skill extract-parameters-from-fullgit 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/extract-parameters-from-full)<a href="https://agentmods.dev/skills/planexeorg/planexe/extract-parameters-from-full"><img src="https://agentmods.dev/badge/skills/planexeorg/planexe/extract-parameters-from-full.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 | $0.00043 | $0.00886 |
| Opus 5 | $0.00022 | $0.00443 |
| Sonnet 5 | $0.00009 | $0.00177 |
| Haiku 4.5 | $0.00004 | $0.00089 |
Grade A, and why
extract-parameters-from-full 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extract Parameters from a PlanExe Report
Overview
Wraps the quantitative-triage system prompt at system-prompt.txt (next to this file) and applies it to a PlanExe report the user supplies. Output is strict JSON matching the schema in the system prompt — no markdown, no commentary.
When to Use
- User says "extract parameters", "extract modelling values", "pull key variables", or similar from a PlanExe report
- User points at a PlanExe report file (typically HTML, may be 100KB–1MB+) and wants structured input for downstream simulation
- User wants a triage list of values that would matter for Monte Carlo or sensitivity analysis
Not for: full report summarisation, narrative analysis, code generation. The system prompt explicitly forbids those.
Workflow
- Get the report path. If the user did not provide one, ask. Do not guess.
- Read
system-prompt.txt(sibling of this SKILL.md). Treat its contents as the authoritative extraction instructions — every rule, hard limit, and schema constraint applies. - Read the report file. For large HTML reports, read the whole file; the system prompt's hard limits (≤8 key_values, ≤5 of each list, ≤25-word comments) keep output bounded regardless of input size.
- Produce the JSON following the exact schema at the end of
system-prompt.txt. Apply every "Important", "Additional modelling rules", and "Formula and dependency rules" section as you generate each field. - Output destination. Default: print JSON to the chat. If the user asks for a file, write to the path they specify. If they want a default file path, suggest
<report-basename>.parameters.jsonnext to the report.
Hard Rules (from system-prompt.txt — re-stated for emphasis)
- JSON only. No markdown fences, no prose, no explanation before or after.
- Percentages as fractions between 0 and 1 with
unit: "fraction". Nevervalue: 60for 60%. - No invented ids in
formula_hint— every variable must be declared inkey_values,missing_values_to_estimate, or the object's owndepends_on. - Every entry with a non-null
formula_hintMUST also declareoutput_name(snake_case id of the computed value) andoutput_unit(e.g."DKK","people","fraction"). Downstream consumers — generate-calculations, run-scenarios, monte-carlo — read these directly and do not parseformula_hintor pattern-match on tokens. The LLM is the single authority for both fields. - Prefer missing-but-needed values over minor explicit values. Don't dump every budget line.
- Clean
source_text— strip citations, footnote markers, replacement chars, UI artifacts.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 52 lines · 43 tokens per session scan A 94b69e03658a
extract-parameters-from-full is a skill published in the GitHub repository PlanExeOrg/PlanExe (398 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 886 once invoked, about $0.0002 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.
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