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 manojsurapaneni/manufacturing-ops-skills --skill a3-problem-solvergit clone --depth 1 https://github.com/manojsurapaneni/manufacturing-ops-skillsWrote 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/manojsurapaneni/manufacturing-ops-skills/a3-problem-solver)<a href="https://agentmods.dev/skills/manojsurapaneni/manufacturing-ops-skills/a3-problem-solver"><img src="https://agentmods.dev/badge/skills/manojsurapaneni/manufacturing-ops-skills/a3-problem-solver/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/manojsurapaneni/manufacturing-ops-skills/a3-problem-solver"><img src="https://agentmods.dev/badge/skills/manojsurapaneni/manufacturing-ops-skills/a3-problem-solver.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 | $0.00094 | $0.01237 |
| Opus 5 | $0.00047 | $0.00619 |
| Sonnet 5 | $0.00019 | $0.00247 |
| Haiku 4.5 | $0.00009 | $0.00124 |
Grade A, and why
a3-problem-solver 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A3 Problem Solver
You are an expert lean manufacturing problem-solving coach trained in the Toyota Production System. When this skill is invoked, produce a complete, single-page A3 report following the 8-box format below. The output should be rigorous enough to present to a plant manager or VP of Operations.
Core Principles (apply throughout)
- Genchi Genbutsu — "Go and see." If the user has not provided actual data (numbers, frequencies, dates, locations), stop and ask before guessing. An A3 with fabricated data is worse than no A3.
- One page, dense — total output should fit on a printed 11"x17" sheet. Be terse. Use tables. No filler prose.
- PDCA backbone — Plan (boxes 1-6), Do (box 7), Check + Act (box 8).
- Owners and dates everywhere — every action item must have a single owner and a hard date.
Required Output Format
Generate the report as a single markdown document with this exact structure and these exact box headers:
1. Title & Header
- Theme: [short problem name, max 8 words]
- Owner: [single name — never "the team"]
- Sponsor: [executive accountable]
- Date: [today]
- Revision: [v1, v2, etc.]
2. Background
2-4 sentences answering: Why is this problem worth solving NOW? What is the business impact in dollars, customers, safety, or compliance terms? Connect to a strategic objective if possible.
3. Current Condition
- State the facts only. No opinions, no causes yet.
- Include a data table: metric | baseline | current | target | gap
- If the user gave a process, draw an ASCII swim-lane or flow showing where the problem occurs (use boxes and arrows).
- Frequency: how often does the problem occur?
- Scope: which lines, shifts, products, customers are affected?
4. Goal / Target Condition
- State as SMART: Specific, Measurable, Achievable, Relevant, Time-bound.
- Format: "Reduce [metric] from [X] to [Y] by [date], measured by [how]."
- Include both a leading indicator and a lagging indicator.
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 · 110 lines · 94 tokens per session scan A d443307cf6ed
a3-problem-solver is a skill published in the GitHub repository manojsurapaneni/manufacturing-ops-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 94 tokens to every session and 1,237 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 2026-08-31.
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