Algo Mfg Doe

What it does

When your agent uses it

• The user needs to systematically test factor effects
• Optimize a manufacturing process
• Determine which variables matter most — even if they say which factors affect quality
• Optimize process settings

Example prompts

• “which factors affect quality”
• “optimize process settings”
• “design an experiment”
• “/algo-mfg-doe”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

1. 1Input Validation
2. 2Core Algorithm
3. 3Verification
4. 4Output

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 json).

  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

Algo Mfg Doe loads about 1.2k tokens when it runs, and up to ~6.7k if it reads all its reference files. Until then it costs about 93 tokens; SKILL.md has 426 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~93

When it runs · the whole SKILL.md, loaded when a task matches

~1.2k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~6.7k

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

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). 426 words, ~1,194 tokens.

name

algo-mfg-doe

description

Design and analyze factorial experiments to identify significant process factors and optimize settings. Use this skill when the user needs to systematically test factor effects, optimize a manufacturing process, or determine which variables matter most — even if they say 'which factors affect quality', 'optimize process settings', or 'design an experiment'.

metadata.category

WP-48 製造演算法

metadata.tags

manufacturing, doe, factorial-design, optimization

IRON LAW: One-Factor-At-A-Time (OFAT) MISSES Interactions
Changing one factor while holding others fixed cannot detect
interactions (where the effect of A depends on the level of B).
Full factorial or fractional factorial designs test ALL main effects
AND interactions in fewer runs than OFAT. A 2³ factorial (8 runs)
gives more information than 6 OFAT runs at lower cost.

{
  "significant_factors": [{"factor": "temperature", "effect": 12.5, "p_value": 0.001}, {"factor": "pressure", "effect": -8.2, "p_value": 0.008}],
  "interactions": [{"factors": "temperature×time", "effect": 5.1, "p_value": 0.03}],
  "optimal": {"temperature": 180, "pressure": 50, "time": 30, "predicted_response": 95.2},
  "metadata": {"design": "2^3_full_factorial", "runs": 8, "replicates": 2, "r_squared": 0.94}
}

© 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 algo-mfg-doe of asgard-ai-platform/skills.

• SKILL.md
• examples/sample_scenario.md
• references/fractional-tables.md
• references/rsm.md

Open the folder on GitHubat commit 4e7f4f8