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Agent skill

Algo Mfg Spc

by asgard-ai-platform in asgard-ai-platform/skills

Implement Statistical Process Control charts to monitor production process stability.

MITAuto-check passedData & Analytics

Install Algo Mfg Spc

skills CLI
$ npx skills add asgard-ai-platform/skills --skill algo-mfg-spc -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install asgard-ai-platform/skills algo-mfg-spc --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-mfg-spc .claude/skills/algo-mfg-spc && rm -rf skills-src

Use ~/.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/

Facts

Skill name
algo-mfg-spc
GitHub stars
242
Token cost
~1.1k tokens
SKILL.md length
429 words
Files
4 (incl. references)
Skills in repo
207
Repo updated
First seen
Licence
MIT

At a glance

Implement Statistical Process Control charts to monitor production process stability.

  • Works in 4 steps: Input Validation → Core Algorithm → Verification → …
  • The user needs to detect process shifts
  • SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Algo Mfg Spc is an agent skill from asgard-ai-platform/skills. Implement Statistical Process Control charts to monitor production process stability. Use this skill when the user needs to detect process shifts, set control limits, or distinguish common cause from special cause variation — even if they say 'process monitoring', 'control chart', or 'is our process in control'.

Its SKILL.md is about 1.1k 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/chart-constants.md` and `references/we-rules.md`).

It sits in Data & Analytics. 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 detect process shifts
  • Set control limits
  • Distinguish common cause from special cause variation — even if they say process monitoring
  • Is our process in control

Example prompts

  • “process monitoring”
  • “control chart”
  • “is our process in control”
  • “/algo-mfg-spc”

Workflow steps

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

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

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 Spc loads about 1.1k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 429 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.2k

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). 429 words, ~1,107 tokens.

Download SKILL.mdSave it as .claude/skills/algo-mfg-spc/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
algo-mfg-spc
description
Implement Statistical Process Control charts to monitor production process stability. Use this skill when the user needs to detect process shifts, set control limits, or distinguish common cause from special cause variation — even if they say 'process monitoring', 'control chart', or 'is our process in control'.
metadata.category
WP-48 製造演算法
metadata.tags
manufacturing, spc, control-chart, quality

Statistical Process Control

Overview

SPC uses control charts to monitor process stability over time. Upper and Lower Control Limits (UCL/LCL) are set at ±3σ from the process mean. Points within limits = common cause variation (stable). Points outside or showing patterns = special cause variation (investigate). Primary charts: X-bar/R, X-bar/S, I-MR, p-chart, c-chart.

When to Use

Trigger conditions:

  • Monitoring production process for stability and detecting shifts
  • Setting statistically-based control limits for quality metrics
  • Distinguishing normal variation from assignable causes

When NOT to use:

  • For process capability assessment (use Cpk)
  • For root cause analysis of known problems (use fishbone/5-why)

Algorithm

IRON LAW: Control Limits Are NOT Specification Limits
Control limits (±3σ) describe what the process IS doing.
Specification limits describe what the process SHOULD do.
A process can be in statistical control (stable) but still produce
out-of-spec products (incapable). Conversely, a capable process may
be out of control (drifting). Monitor control FIRST, then assess capability.
Phase 1: Input Validation

Collect: 25+ subgroups of measurements (5 per subgroup typical for X-bar/R). Verify: measurement system is adequate (gauge R&R < 10%), data collected in time order. Gate: Sufficient subgroups, time-ordered data, measurement system verified.

Phase 2: Core Algorithm

X-bar/R Chart (subgroup data):

  1. Compute subgroup means (X̄) and ranges (R)
  2. Compute grand mean (X̄̄) and average range (R̄)
  3. UCL_X̄ = X̄̄ + A₂×R̄, LCL_X̄ = X̄̄ - A₂×R̄ (A₂ from statistical tables by subgroup size)
  4. UCL_R = D₄×R̄, LCL_R = D₃×R̄
  5. Plot points, apply Western Electric rules for out-of-control signals
Phase 3: Verification

Check for: points outside limits, runs (7+ consecutive on one side), trends (7+ consecutive increasing/decreasing), 2 of 3 beyond 2σ, 4 of 5 beyond 1σ. Gate: Chart constructed, out-of-control signals identified.

Phase 4: Output

Return control chart data with signals and stability assessment.

Output Format

json
{
  "chart": {"type": "xbar_r", "center_line": 50.2, "ucl": 52.1, "lcl": 48.3},
  "signals": [{"subgroup": 18, "rule": "point_beyond_ucl", "value": 52.8}],
  "stability": "out_of_control",
  "metadata": {"subgroups": 30, "subgroup_size": 5}
}

Examples

Sample I/O

Input: 25 subgroups of 5 measurements each, all within ±3σ, no patterns Expected: Process in control. No signals triggered.

Show full SKILL.md (172 more words)Show less
Edge Cases
InputExpectedWhy
One point just outside UCLSignal, but may be false alarm~0.27% chance per point even when in control
Gradual upward trendTrend rule triggeredProcess drifting, investigate
All points near centerSuspicious — check dataMay indicate data manipulation or measurement issue

Gotchas

  • Rational subgrouping: Subgroups must be collected under similar conditions (same shift, machine, operator). Poor subgrouping inflates within-group variation, making limits too wide.
  • Recalculating limits: Don't recalculate limits every time you add data. Establish limits from a stable baseline period and keep them fixed until a known process change.
  • Chart type selection: Variables data (measurements) → X-bar/R or I-MR. Attribute data (counts/proportions) → p-chart, np-chart, c-chart, u-chart. Wrong chart type = wrong limits.
  • Normality assumption: X-bar chart is robust to non-normality (central limit theorem). Individual charts (I-MR) require approximate normality — check with histogram.
  • Over-adjustment: Reacting to every small variation (tampering) INCREASES variability. Only investigate special cause signals, not common cause variation.

References

  • For control chart constants tables, see references/chart-constants.md
  • For Western Electric rules and pattern detection, see references/we-rules.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 algo-mfg-spc of asgard-ai-platform/skills.

  • SKILL.md
  • examples/sample_scenario.md
  • references/chart-constants.md
  • references/we-rules.md

Open the folder on GitHubat commit 4e7f4f8

Compare with similar skills

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TimesFM Forecastinggoogle-research/timesfm34k—~4.7kAutomated safety check: PassApache-2.0

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Categories

Questions about Algo Mfg Spc

What does Algo Mfg Spc do?

Implement Statistical Process Control charts to monitor production process stability. Algo Mfg Spc is an agent skill from asgard-ai-platform/skills. Implement Statistical Process Control charts to monitor production process stability.

When should I use Algo Mfg Spc?

Algo Mfg Spc fits situations like: the user needs to detect process shifts; set control limits; distinguish common cause from special cause variation — even if they say process monitoring; is our process in control.

How do I install Algo Mfg Spc in Claude Code?

Run `npx skills add asgard-ai-platform/skills --skill algo-mfg-spc -a claude-code`. Or copy the skill folder (algo-mfg-spc in asgard-ai-platform/skills) into .claude/skills/algo-mfg-spc in your project. Claude Code loads it when a task matches its description.

How do I install Algo Mfg Spc in Codex?

Run `npx skills add asgard-ai-platform/skills --skill algo-mfg-spc -a codex`. Or copy the skill folder (algo-mfg-spc in asgard-ai-platform/skills) into .agents/skills/algo-mfg-spc in your project. Codex loads it when a task matches its description.

Can I use Algo Mfg Spc 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 algo-mfg-spc -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-mfg-spc, .gemini/skills/algo-mfg-spc, .github/skills/algo-mfg-spc and .opencode/skills/algo-mfg-spc in your project.

What does Algo Mfg Spc need to run?

SKILL.md names no scripts, command-line tools or credentials: Algo Mfg Spc is instructions for the agent only.

Does Algo Mfg Spc 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 Algo Mfg Spc 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 Algo Mfg Spc use?

Algo Mfg Spc 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 Algo Mfg Spc use?

About 1.1k tokens (SKILL.md is roughly 4.4k 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 5k tokens, read only when the agent opens those files.

What are the alternatives to Algo Mfg Spc?

Skills that share tags, products or a category with Algo Mfg Spc: Matplotlib (zLanqing/codex-claude-academic-skills, 4.7k stars), Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars) and Chart Visualization (bytedance/deer-flow, 84k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Algo Mfg Spc?

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.