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 skills add PlanExeOrg/PlanExe --skill generate-boundsgit 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/generate-bounds)<a href="https://agentmods.dev/skills/planexeorg/planexe/generate-bounds"><img src="https://agentmods.dev/badge/skills/planexeorg/planexe/generate-bounds/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/generate-bounds"><img src="https://agentmods.dev/badge/skills/planexeorg/planexe/generate-bounds.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00042 | $0.02088 |
| Opus 5.5 | $0.00017 | $0.00835 |
| Sonnet 5.5 | $0.00008 | $0.00418 |
| Haiku 4.5 | $0.00004 | $0.00209 |
Grade A, and why
generate-bounds 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Bounds for Extracted Parameters
Overview
Wraps the bounds-estimator system prompt at system-prompt.txt (next to this file) and applies it to a parameter JSON produced by extract-parameters-from-full (and ideally already passed validate-parameters). Output is a strict JSON object keyed by variable id, with low / base / high / unit / rationale / source for every variable that needs an assumption range.
Stage 4 of the pipeline described in planexe_simulator/README.md.
When to Use
- User asks to "generate bounds", "estimate ranges", "add low/base/high", or "prepare for scenarios" given an extract-parameters-from-full JSON
- User wants to fill in assumptions for
missing_values_to_estimateand uncertainkey_valuesbefore running deterministic scenarios or Monte Carlo - Pipeline step between
validate-parameters(passes clean) andgenerate-calculations/run-scenarios
Not for: regenerating the parameter JSON (use extract-parameters-from-full), validating the JSON (use validate-parameters), or producing Python code (use generate-calculations).
Workflow
- Get the input JSON path. If the user did not provide one, ask. Do not guess.
- Read
system-prompt.txt(sibling of this SKILL.md). Its selection rules and spread heuristics are authoritative. - Read the parameter JSON. Assume it has already passed
validate-parameters; if it visibly hasn't, tell the user and offer to validate first. - Produce the bounds JSON per the system prompt.
- Self-audit before output. For each bound entry, verify:
- Every numbered citation in
rationale(Risk N, Issue N, Decision N) refers to content that substantively supports the claim — not just lexically present. - For variables feeding a declared gate threshold, the base-vs-threshold relationship is consistent with the rationale. If base implies base-case gate failure, the rationale names a report-internal anchor that justifies the shift.
source: "data"entries name an explicit anchor in the rationale.- For asymmetric bounds, the rationale notes the asymmetry's source. If any check fails, revise the bound; do not ship inconsistent state.
- Every numbered citation in
- Output destination. Default: print the JSON to chat. If the user asks for a file, write to the path they specify. Suggested default file path:
<input-basename>.bounds.jsonnext to the input.
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.
- 1mo ago First seen · 118 lines · 42 tokens per session scan A 6ce0ca68dc13
generate-bounds is a skill published in the GitHub repository PlanExeOrg/PlanExe (402 stars, last pushed 3d ago), licensed MIT. It adds 42 tokens to every session and 2,088 once invoked, about $0.0002 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 2026-08-30.
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