spc

name

spc

description

Build or analyze control charts and process capability studies by calling the quality-platform SPC MCP tools — X-bar/R, X-bar/S, I-MR, p/c/u attribute charts, EWMA and CUSUM, Western Electric/Nelson rule detection, Phase I/II baseline freezing, and Cp/Cpk/Pp/Ppk capability with its stability and normality gates. Use for control-chart, process-stability and process-capability requests, not FMEA risk scoring or Gage R&R measurement-system analysis.

spc — chart a process and study its capability

Run an SPC workflow by calling the quality-platform MCP tools. The engine owns every number; this skill decides which tool to call, in what order, and how to present what comes back. See skills/CONVENTIONS.md for the rules this skill is written to.

When to use

The user is looking at process measurements over time — a control chart, a run-rule question, a Phase I baseline, or a capability study against spec limits. Pick the tool family by the shape of the data:

What the user has	Tool family
Variables data in subgroups, or one measurement per point	spc_xbar_r / spc_xbar_s / spc_imr
Attribute data — defectives or defect counts	spc_p / spc_c / spc_u
A hunt for a small sustained shift	spc_ewma / spc_cusum
An accepted baseline to monitor new data against	spc_freeze_*, then spc_apply_*
A question about out-of-control signals	spc_detect_we_violations or spc_detect_nelson_violations
Spec limits and a "is this process capable?" question	spc_assess_stability, then spc_capability

Which chart for which subgroup size, when EWMA or CUSUM is the right answer instead of a Shewhart chart, and every precondition a capability study carries live in references/spc-method-notes.md — read it before routing, rather than guessing from the table above.

Two routing rules that are not judgement calls:

• Western Electric and Nelson are mutually exclusive. Pick one rule set for a chart; never report both (apps/spc/docs/ASSUMPTIONS_LOG.md RULE 8).
• Never run either detector on an EWMA or CUSUM chart. Those two statistics are autocorrelated by construction, so run rules produce systematic false alarms; they signal only on their own limit or decision-interval crossings (RULE 15). Refuse and explain rather than calling the tool — the MCP layer does not gate this for you.

This is not the skill for FMEA risk scoring (Risk Priority Number, Action Priority) or for Gage R&R measurement systems analysis — those have their own tools on the same server.

Steps

1. Connect to the quality-platform MCP server over stdio. The host normally has it configured already; if not, it launches as python -m mcp_app.server from the workspace root.
2. Establish the data shape before choosing anything: variables or attribute, the subgroup size, and whether this is a first retrospective look (Phase I) or monitoring against an already-accepted baseline (Phase II). Route per references/spc-method-notes.md; ask the user rather than inferring a subgroup size or a chart type from the numbers.
3. For run-rule detection, take cl and sigma from the chart tool's own result. On an X-bar chart sigma is the sigma of the plotted points — the chart's sigma_hat divided by the square root of the subgroup size — not sigma_hat itself. It comes from the engine's result, never from a fresh pass over the raw data.
4. For a capability study, collect LSL and/or USL from the user (at least one is needed for an index) and establish stability first: call spc_assess_stability, or a chart tool followed by spc_detect_we_violations, and pass the resulting signal list as spc_capability's violations. Omitting it leaves stable null, which means not assessed — report it that way and never read it as in control. Let force_method="auto" choose the distribution path, or call spc_normality_test if the user asks the normality question directly; do not pre-judge normality yourself.
5. Call the chosen tool(s) and report what came back verbatim — the limits, the signal list, the indices, the confidence intervals and every null among them. Never round, re-derive an index from another one, or run a "sanity check" calculation; a second opinion computed here is a defect, not a safeguard.
6. On a tool error — ragged or empty subgroups, an untabulated subgroup size, a non-finite or non-positive sample size, a sigma at or below zero, constant data, fewer than three observations, or a frozen baseline that does not match the new data's chart type — surface the message to the user and ask for corrected input. Never clamp a limit, drop a point, or substitute a default for a value the user did not give.

Worked example

User: "Here are ten subgroups of four parts each. Spec is 9.8 to 10.8 — is the process capable?"

Three calls, in order:

1. spc_xbar_r with the ten subgroups. The engine returns xbarbar=10.355, rbar=0.24999999999999983, ucl_x=10.53725, lcl_x=10.17275 and sigma_hat=0.12141816415735784.
2. spc_detect_we_violations with the subgroup means as points, cl=10.355, and sigma set to the chart's plotted-point sigma (sigma_hat over the square root of 4, 0.06070908207867892). It returns [] — no Western Electric signal on this baseline.
3. spc_capability with the same ten subgroups as data, lsl=9.8, usl=10.8, and violations=[] — the empty list from step 2, which is what states "assessed, in control". Abridged to the fields this example turns on; the engine also returns the method-selection fields (normal_before, lambda_used, fitted_dist, note and the rest), and a real report passes on every field it sent:

{
  "method": "normal",
  "cp": 1.3726666666666678,
  "cpk": 1.2216733333333352,
  "pp": 1.1947035802691601,
  "ppk": 1.063286186439553,
  "pp_ci": [0.9304287839813086, 1.4584477931703752],
  "ppk_ci": [0.8056991824051143, 1.3208731904739919],
  "ppk_lower": 0.8471123581530918,
  "ci_estimator": "sample_sd_ddof1",
  "ci_df": 39,
  "mean": 10.355,
  "sigma_hat": 0.12141816415735784,
  "sigma_overall" 
   
   
   
   

Report those fields as engine output. The cpk value of 1.2216733333333352 falls in the "Marginal — reduce variation before release-critical use" tier for 1.00-1.32 in apps/spc/docs/ASSUMPTIONS_LOG.md RULE 6; stable is true because the caller supplied an empty violation list, which the engine takes as chart context rather than deriving one (RULE 7). The absence of confidence intervals on cp/cpk is expected on this path, not a gap — only Pp and Ppk carry intervals here, tagged by ci_estimator and ci_df (RULE 14). Nothing above is re-derived in this skill.

scripts/call_spc_capability.py is the capability call as a runnable script, for when a shell call is cheaper than a tool call.

Reference

• references/mcp-tool-contract.md — the tool namespace convention, the request/response shape of every SPC tool (charts, freeze/apply, rule detection, capability, normality, stability), and the error contract.
• references/spc-method-notes.md — the full chart-selection matrix, Phase I versus Phase II, the EWMA/CUSUM parameter notes, and the capability preconditions, each cited to a rule in apps/spc/docs/ASSUMPTIONS_LOG.md.