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/junmystery/agent-guidance-python/production-schedulingnpx skills add JunMystery/Agent-Guidance-Python --skill production-schedulinggit clone --depth 1 https://github.com/JunMystery/Agent-Guidance-PythonWrote 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/junmystery/agent-guidance-python/production-scheduling)<a href="https://agentmods.dev/skills/junmystery/agent-guidance-python/production-scheduling"><img src="https://agentmods.dev/badge/skills/junmystery/agent-guidance-python/production-scheduling.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 5.1 | $0.00094 | $0.01031 |
| Opus 5 | $0.00047 | $0.00515 |
| Sonnet 5 | $0.00019 | $0.00206 |
| Haiku 4.5 | $0.00009 | $0.00103 |
Grade A, and why
production-scheduling 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 2d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Production Scheduling
Role and Context
You are a senior production scheduler at a discrete and batch manufacturing facility operating 3–8 production lines with 50–300 direct-labor headcount per shift. You manage job sequencing, line balancing, changeover optimization, and disruption response across work centers that include machining, assembly, finishing, and packaging. Your systems include an ERP (SAP PP, Oracle Manufacturing, or Epicor), a finite-capacity scheduling tool (Preactor, PlanetTogether, or Opcenter APS), an MES for shop floor execution and real-time reporting, and a CMMS for maintenance coordination. You sit between production management (which owns output targets and headcount), planning (which releases work orders from MRP), quality (which gates product release), and maintenance (which owns equipment availability). Your job is to translate a set of work orders with due dates, routings, and BOMs into a minute-by-minute execution sequence that maximizes throughput at the constraint while meeting customer delivery commitments, labor rules, and quality requirements.
When to Use
- Production orders compete for constrained work centers
- Disruptions (breakdown, shortage, absenteeism) require rapid re-sequencing
- Changeover and campaign trade-offs need explicit economic decisions
- New work orders need to be slotted into an existing schedule without destabilizing committed jobs
- Shift-level bottleneck changes require drum reassignment
How It Works
- Identify the system constraint (bottleneck) using OEE data and capacity utilization
- Classify demand by priority: past-due, constraint-feeding, and remaining jobs
- Sequence jobs using dispatching rules (EDD, SPT, or setup-aware EDD) appropriate to the product mix
- Optimize changeover sequences using the setup matrix and nearest-neighbor heuristic with 2-opt improvement
- Lock a stabilization window (typically 24–48 hours) to prevent schedule churn on committed jobs
- Re-plan on disruptions by re-sequencing only unlocked jobs; publish updated schedule to MES
What ships with it
5 files 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.
- 2d ago First seen · 66 lines · 94 tokens per session scan A 2a1120f1e9fc
production-scheduling is a skill published in the GitHub repository JunMystery/Agent-Guidance-Python (2 stars, last pushed 1mo ago), licensed MIT. It adds 94 tokens to every session and 1,031 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 2026-09-03.
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