Diagnose Gateway
openclaw/openclaw
Diagnose Gateway, config, secrets, channels, and port failures with read-only one-liners.
Calculate and diagnose Overall Equipment Effectiveness (OEE) by decomposing into Availability, Performance, and Quality rates.
MITAuto-check passed
Install Mfg Oee Analysis
$ npx skills add asgard-ai-platform/skills --skill mfg-oee-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills mfg-oee-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/mfg-oee-analysis .claude/skills/mfg-oee-analysis && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "mfg-oee-analysis" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/mfg-oee-analysis into .claude/skills/mfg-oee-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfg-oee-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/asgard-ai-platform/skills/tree/main/mfg-oee-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add asgard-ai-platform/skills --skill mfg-oee-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills mfg-oee-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/mfg-oee-analysis .agents/skills/mfg-oee-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mfg-oee-analysis" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/mfg-oee-analysis into .agents/skills/mfg-oee-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfg-oee-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill mfg-oee-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills mfg-oee-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/mfg-oee-analysis .cursor/skills/mfg-oee-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "mfg-oee-analysis" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/mfg-oee-analysis into .cursor/skills/mfg-oee-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfg-oee-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/asgard-ai-platform/skills.git --path mfg-oee-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add asgard-ai-platform/skills --skill mfg-oee-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills mfg-oee-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/mfg-oee-analysis .gemini/skills/mfg-oee-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "mfg-oee-analysis" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/mfg-oee-analysis into .gemini/skills/mfg-oee-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfg-oee-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install asgard-ai-platform/skills mfg-oee-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add asgard-ai-platform/skills --skill mfg-oee-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/mfg-oee-analysis .github/skills/mfg-oee-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "mfg-oee-analysis" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/mfg-oee-analysis into .github/skills/mfg-oee-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfg-oee-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill mfg-oee-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install asgard-ai-platform/skills mfg-oee-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/mfg-oee-analysis .opencode/skills/mfg-oee-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "mfg-oee-analysis" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/mfg-oee-analysis into .opencode/skills/mfg-oee-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfg-oee-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
mfg-oee-analysis- GitHub stars
- 242
- Token cost
- ~1.2k tokens
- SKILL.md length
- 329 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Calculate and diagnose Overall Equipment Effectiveness (OEE) by decomposing into Availability, Performance, and Quality rates.
- The user needs to measure production line efficiency
- SKILL.md covers Framework, Output Format, Gotchas and References
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Identify equipment losses
What it does
Mfg Oee Analysis is an agent skill from asgard-ai-platform/skills. Calculate and diagnose Overall Equipment Effectiveness (OEE) by decomposing into Availability, Performance, and Quality rates. Use this skill when the user needs to measure production line efficiency, identify equipment losses, benchmark manufacturing performance, or justify capital investment — even if they say 'why is our output low', 'machine utilization report', 'production efficiency', or 'how much capacity are we losing'.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/oee-automation.md` and `references/tpm.md`).
The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.
When your agent uses it
- The user needs to measure production line efficiency
- Identify equipment losses
- Benchmark manufacturing performance
- Justify capital investment — even if they say why is our output low
Example prompts
- “why is our output low”
- “machine utilization report”
- “production efficiency”
- “/mfg-oee-analysis”
What it can do on your machine
Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Mfg Oee Analysis loads about 1.2k tokens when it runs, and up to ~7.5k if it reads all its reference files. Until then it costs about 112 tokens; SKILL.md has 329 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 329 words, ~1,161 tokens.
Download SKILL.mdSave it as
.claude/skills/mfg-oee-analysis/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- mfg-oee-analysis
- description
- Calculate and diagnose Overall Equipment Effectiveness (OEE) by decomposing into Availability, Performance, and Quality rates. Use this skill when the user needs to measure production line efficiency, identify equipment losses, benchmark manufacturing performance, or justify capital investment — even if they say 'why is our output low', 'machine utilization report', 'production efficiency', or 'how much capacity are we losing'.
- metadata.category
- WP-03 製造業
- metadata.tags
- manufacturing, oee, production-efficiency
OEE Analysis
Framework
IRON LAW: OEE = Availability × Performance × Quality
OEE is a MULTIPLICATIVE metric. 90% × 90% × 90% = 72.9%, not 90%.
Each factor compounds the loss. World-class OEE is 85%+. Most plants
operate at 60-65%. Knowing the TOTAL is useless — you must decompose
to find which factor is dragging performance down.The Three Factors
| Factor | Formula | Measures | Loss Categories |
|---|---|---|---|
| Availability | Run Time / Planned Production Time | Uptime vs downtime | Equipment failures, changeovers, material shortages |
| Performance | (Ideal Cycle Time × Total Count) / Run Time | Actual speed vs design speed | Minor stops, slow running, idling |
| Quality | Good Count / Total Count | Yield, first-pass quality | Defects, rework, scrap, startup rejects |
Six Big Losses (mapped to OEE factors)
| Loss | OEE Factor | Example |
|---|---|---|
| 1. Equipment failure | Availability | Machine breakdown, unplanned repair |
| 2. Setup & changeover | Availability | Product changeover, die change, cleaning |
| 3. Idling & minor stops | Performance | Sensor blockage, jam clearing, small adjustments |
| 4. Reduced speed | Performance | Running below rated speed due to wear or material |
| 5. Process defects | Quality | In-process rejects, rework |
| 6. Startup rejects | Quality | Scrap during warm-up, first-article failures |
Calculation Example
Planned Production Time: 480 min (8-hour shift)
Downtime (breakdowns + changeover): 60 min
Run Time: 420 min
Ideal Cycle Time: 1 min/unit
Total Units Produced: 380
Good Units: 360
Defective Units: 20
Availability = 420 / 480 = 87.5%
Performance = (1 × 380) / 420 = 90.5%
Quality = 360 / 380 = 94.7%
OEE = 87.5% × 90.5% × 94.7% = 75.0%Diagnosis Steps
Phase 1: Calculate OEE for each production line/machine Phase 2: Identify the weakest factor (Availability, Performance, or Quality) Phase 3: Pareto the losses within that factor (which specific loss is biggest?) Phase 4: Root cause analysis on the top loss (5 Whys, fishbone) Phase 5: Improve and remeasure
Benchmarks
| OEE Level | Rating | Typical |
|---|---|---|
| > 85% | World-class | Top manufacturers |
| 60-85% | Typical | Room for improvement |
| 40-60% | Low | Significant losses, urgent action needed |
| < 40% | Critical | Equipment or process fundamentally broken |
Output Format
# OEE Report: {Production Line}
## OEE Summary
| Factor | Value | Benchmark | Status |
|--------|-------|-----------|--------|
| Availability | {%} | >90% | 🟢/🟡/🔴 |
| Performance | {%} | >95% | 🟢/🟡/🔴 |
| Quality | {%} | >99% | 🟢/🟡/🔴 |
| **OEE** | **{%}** | **>85%** | 🟢/🟡/🔴 |
## Loss Breakdown
| Loss | Minutes Lost | % of Total Loss | Priority |
|------|-------------|----------------|---------|
| {loss type} | {min} | {%} | 1/2/3 |
## Root Cause (Top Loss)
{5 Whys or fishbone analysis}
## Improvement Plan
| Action | Target Impact | Timeline | Owner |
|--------|-------------|----------|-------|
| {action} | +{X%} OEE | {weeks} | {who} |Gotchas
- OEE is per machine, not per plant: Plant-level OEE averages hide that one machine at 95% and another at 45% average to 70%. Analyze individually.
- Planned downtime is excluded: OEE measures losses against PLANNED production time. Scheduled maintenance, no-production shifts, and planned shutdowns are excluded from the denominator.
- 100% OEE is not the goal: It would mean zero changeovers, zero defects, running at max speed 100% of the time. Pursuing 100% can increase costs (e.g., never doing preventive maintenance). Target 85%+ for critical lines.
- Data collection is the real challenge: Manual OEE tracking is inaccurate. Invest in automated data collection (sensors, MES integration) for reliable measurement.
References
- For TPM (Total Productive Maintenance) methodology, see
references/tpm.md - For automated OEE data collection, see
references/oee-automation.md
© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (references) in mfg-oee-analysis of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/oee-automation.md
- references/tpm.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Mfg Oee Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Mfg Oee Analysis this skillasgard-ai-platform/skills | 242 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Diagnose Gatewayopenclaw/openclaw | 392k | — | ~670 | Automated safety check: Pass | MIT | |
| Diagnosegithub/awesome-copilot | 40k | 1 repos | ~1k | Automated safety check: Pass | MIT | |
| Add Effectremotion-dev/remotion | 63k | — | ~2.8k | Automated safety check: Pass | Custom licence | |
| Effect V4ComposioHQ/composio | 30k | — | ~1k | Automated safety check: Pass | MIT | |
| Parallax Effectsthedaviddias/Front-End-Checklist | 74k | — | ~523 | Automated safety check: Pass | MIT |
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Questions about Mfg Oee Analysis
What does Mfg Oee Analysis do?
Calculate and diagnose Overall Equipment Effectiveness (OEE) by decomposing into Availability, Performance, and Quality rates. Mfg Oee Analysis is an agent skill from asgard-ai-platform/skills. Calculate and diagnose Overall Equipment Effectiveness (OEE) by decomposing into Availability, Performance, and Quality rates.
When should I use Mfg Oee Analysis?
Mfg Oee Analysis fits situations like: the user needs to measure production line efficiency; identify equipment losses; benchmark manufacturing performance; justify capital investment — even if they say why is our output low.
How do I install Mfg Oee Analysis in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill mfg-oee-analysis -a claude-code`. Or copy the skill folder (mfg-oee-analysis in asgard-ai-platform/skills) into .claude/skills/mfg-oee-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Mfg Oee Analysis in Codex?
Run `npx skills add asgard-ai-platform/skills --skill mfg-oee-analysis -a codex`. Or copy the skill folder (mfg-oee-analysis in asgard-ai-platform/skills) into .agents/skills/mfg-oee-analysis in your project. Codex loads it when a task matches its description.
Can I use Mfg Oee Analysis in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgard-ai-platform/skills --skill mfg-oee-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mfg-oee-analysis, .gemini/skills/mfg-oee-analysis, .github/skills/mfg-oee-analysis and .opencode/skills/mfg-oee-analysis in your project.
What does Mfg Oee Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Mfg Oee Analysis is instructions for the agent only.
Does Mfg Oee Analysis access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Mfg Oee Analysis safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Mfg Oee Analysis use?
Mfg Oee Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Mfg Oee Analysis use?
About 1.2k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.3k tokens, read only when the agent opens those files.
What are the alternatives to Mfg Oee Analysis?
Skills that share tags, products or a category with Mfg Oee Analysis: Diagnose Gateway (openclaw/openclaw, 392k stars), Diagnose (github/awesome-copilot, 40k stars), Add Effect (remotion-dev/remotion, 63k stars) and Effect V4 (ComposioHQ/composio, 30k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mfg Oee Analysis?
asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.
Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.