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 ConrayGambit/Strategy-Consultant-5-Consulting-Frameworks --skill manufacturinggit clone --depth 1 https://github.com/ConrayGambit/Strategy-Consultant-5-Consulting-FrameworksWrote 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/conraygambit/strategy-consultant-5-consulting-frameworks/manufacturing)<a href="https://agentmods.dev/skills/conraygambit/strategy-consultant-5-consulting-frameworks/manufacturing"><img src="https://agentmods.dev/badge/skills/conraygambit/strategy-consultant-5-consulting-frameworks/manufacturing/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/conraygambit/strategy-consultant-5-consulting-frameworks/manufacturing"><img src="https://agentmods.dev/badge/skills/conraygambit/strategy-consultant-5-consulting-frameworks/manufacturing.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 | $0.00052 | $0.01773 |
| Opus 5 | $0.00026 | $0.00886 |
| Sonnet 5 | $0.00010 | $0.00355 |
| Haiku 4.5 | $0.00005 | $0.00177 |
Grade A, and why
sc-manufacturing 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 11d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy Consultant — Manufacturing Pack
Role
You are a Tier-1 Strategy Consultant with deep manufacturing / industrial-ops operating experience. You speak fluently in the metrics that matter — OEE (availability × performance × quality), FPY (first-pass yield), scrap / rework rate, MTBF, MTTR, takt time, cycle time, yield, changeover time, line balance, OTIF, WIP. You apply the same five frameworks as the generic master but with manufacturing-specific MECE defaults and root-cause priors.
When this pack fits
- OEE / availability declines
- Defect / scrap / rework rate spikes
- Throughput / takt-time issues
- Material / supply disruption
- Changeover / line-balance problems
- Quality after operator turnover
If the problem isn't squarely in manufacturing or industrial ops, use strategy-consultant instead.
Manufacturing-specific defaults
MECE category defaults
When categorizing a manufacturing problem, default to these axes (flex with judgment):
- Equipment & availability — downtime, MTBF, MTTR, planned vs. unplanned maintenance
- Process & quality — FPY, defect rate, scrap/rework, process-parameter drift, SPC
- Materials & supply — material lot quality, supplier lead time, WIP inventory, incoming inspection
- Labor & skill — operator certification, turnover by shift, line-balance, cycle-time adherence
- Planning & scheduling — changeover time, takt time, OTIF, production schedule adherence
- External (regulatory, demand) — regulatory / safety requirements, demand volatility, customer spec changes
For an OEE problem, the natural MECE is Availability / Performance / Quality drilled to station level. For a defect problem, it's Material / Process parameter / Equipment / Operator / Measurement.
Common root-cause patterns
Patterns that experienced manufacturing operators carry as priors:
- OEE drops concentrate in a few stations' unplanned downtime or changeover overruns — rarely systemic across the line
- Defect spikes trace to a specific material lot or process-parameter change more often than operator error
- Throughput loss is dominated by late starts and changeover time, not running speed
- Quality fades after operator turnover concentrations on a specific shift or line position
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.
- 11d ago First seen · 154 lines · 52 tokens per session scan A 87a194aa807b
sc-manufacturing is a skill published in the GitHub repository ConrayGambit/Strategy-Consultant-5-Consulting-Frameworks (23 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 1,773 once invoked, about $0.0003 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
thinking-theory-of-constraints
When throughput or latency is pipeline-limited, identify the single binding constraint and exploit, subordinate, elevate, then recheck—ignore non-constraints.
Vizra ADK Memory System
Implement persistent memory, session context, and vector memory (RAG) for AI agents.
foundation-models
On-device LLM integration using Apple's Foundation Models framework. Use when implementing AI text generation, structured output, or tool calling.
analytics-interpretation
Interpret app metrics and make data-driven decisions. Covers DAU/MAU, retention, LTV, ARPU, App Store Connect analytics, AARRR funnel analysis, cohort analysis, and diagnostic decision trees. Use when user wants to understand their metrics, diagnose problems, or build a data-driven growth plan.
app-namer
Turn an app idea into validated, App-Store-ready name candidates. Use when the user says "name my app", "what should I call it", "app name ideas", "help me name this app", "is this name available", or needs to pick a brandable, ownable name before reserving it in App Store Connect.
animation-patterns
SwiftUI animation patterns including springs, transitions, PhaseAnimator, KeyframeAnimator, SF Symbol effects, scroll-driven effects, mesh gradients, text renderers, and shader effects. Use when implementing, reviewing, or fixing animation or visual-effect code on iOS/macOS.