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 RBraga01/Quality-Engineering-Skills --skill spc-control-chartsgit clone --depth 1 https://github.com/RBraga01/Quality-Engineering-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/rbraga01/quality-engineering-skills/spc-control-charts)<a href="https://agentmods.dev/skills/rbraga01/quality-engineering-skills/spc-control-charts"><img src="https://agentmods.dev/badge/skills/rbraga01/quality-engineering-skills/spc-control-charts/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/rbraga01/quality-engineering-skills/spc-control-charts"><img src="https://agentmods.dev/badge/skills/rbraga01/quality-engineering-skills/spc-control-charts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00093 | $0.02563 |
| Opus 5.5 | $0.00037 | $0.01025 |
| Sonnet 5.5 | $0.00019 | $0.00513 |
| Haiku 4.5 | $0.00009 | $0.00256 |
Grade A, and why
spc-control-charts 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 2mo 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Process Control (SPC)
When to use
Use this skill when:
- Selecting the correct control chart type for a process characteristic
- Interpreting control chart signals — is this special cause or common cause?
- Calculating Cp, Cpk, Pp, Ppk and determining if the process is capable
- Responding to an out-of-control signal on a production line
- Setting up SPC for a new special characteristic (PPAP/APQP requirement)
- Auditing an SPC system for correctness and adequacy
- Explaining SPC results to a customer or during an audit
Prerequisites
- The characteristic to monitor (variable or attribute data)
- Production data (minimum 25 subgroups for control limits, 100+ pieces for capability)
- MSA study completed and %GRR < 30% for variable charts
- Process specification (nominal + tolerance) for capability calculations
- Subgroup size decided based on rational subgrouping principle
Workflow
Step 1 — Select the correct control chart
Variable data (measured values — length, weight, pressure, temperature)
| Chart | When to use |
|---|---|
| X̄-R (X-bar/Range) | Subgroup size 2–9; most common in manufacturing |
| X̄-S (X-bar/Sigma) | Subgroup size ≥ 10; better sensitivity to spread |
| I-MR (Individuals/Moving Range) | Subgroup size = 1; one measurement per inspection (slow processes, destructive tests) |
Attribute data (counts, pass/fail, defect rates)
| Chart | When to use |
|---|---|
| p-chart | Proportion defective; variable subgroup size |
| np-chart | Number defective; constant subgroup size |
| c-chart | Count of defects per unit; constant inspection area |
| u-chart | Defects per unit; variable inspection area |
Decision rule: If you can measure it with a number, use a variable chart. Variable charts are more sensitive and require smaller samples to detect process shifts.
Step 2 — Calculate control limits
X̄-R chart
From at least 25 subgroups of size n:
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 225 lines · 93 tokens per session scan A c602e6f38c28
spc-control-charts is a skill published in the GitHub repository RBraga01/Quality-Engineering-Skills (43 stars, last pushed 7d ago), licensed MIT. It adds 93 tokens to every session and 2,563 once invoked, about $0.0004 per session on Opus 5.5. 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.
spc-control-charts compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| spc-control-chartsRBraga01/Quality-Engineering-Skills | Skill | 43 | 7d ago | MIT | A | 93 | Claude Code, Codex |
| flow-nexus-neuralproffesor-for-testing/agentic-qe | Skill | 525 | yesterday | MIT | A | 21 | Claude Code |
| agent-matrix-optimizerproffesor-for-testing/agentic-qe | Skill | 525 | yesterday | MIT | A | 21 | Claude Code |
| reliability-engineeringxjtulyc/awesome-rosetta-skills | Skill | 35 | 6mo ago | none | A | 33 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |