fix: sync all skill updates and new skills into plugin bundles

- Synced 97 existing skill SKILL.md files from skills/ to their plugin bundle copies
- Added 7 new skills to plugin bundles:
  - seo-content-brief, media-pitch -> pm-gtm
  - tax-planning-checklist -> pm-finance
  - change-management-plan -> pm-hr
  - sales-forecasting-model -> pm-sales
  - workshop-facilitation-guide -> pm-operations
  - teaching-lesson-plan -> pm-cross

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

plugins/pm-advanced/skills/experiment-designer/SKILL.md

@@ -1,55 +1,69 @@
 ---
 name: experiment-designer
-description: Designs A/B tests from hypotheses and interprets experiment results 
-with statistical rigour. Use when user says "run an experiment", "design an A/B 
-test", "test this feature", "interpret these results", "was this experiment 
-successful", or "what sample size do I need".
-metadata:
-  author: Mohit Aggarwal
-  version: 1.0.0
-  category: data-and-metrics
-  tags: [experimentation, data, analytics, ab-testing]
-  documentation: https://github.com/mohitagw15856/pm-claude-skills
+description: "Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation."
 ---
+
 # Experiment Designer Skill
 
-## Purpose
-Produce rigorous experiment designs from product hypotheses, and interpret 
-results with statistical and practical significance — so you can defend every 
-decision to a sceptical engineering lead or data scientist.
+Produce rigorous experiment designs from product hypotheses, and interpret results with statistical and practical significance — so you can defend every decision to a sceptical engineering lead or data scientist.
+
+## Required Inputs
+
+Ask the user for these if not provided:
+**For experiment design:**
+- Hypothesis (what change, what metric, what expected movement)
+- Current baseline metric value
+- Minimum detectable effect (MDE) — the smallest lift worth caring about
+- Available daily sample size
+
+**For results interpretation:**
+- Control and variant results (raw numbers or percentages)
+- P-value or confidence interval
+- Run duration (days)
+- Any anomalies observed during the test
 
 ## Two-Phase Process
 
 ### Phase 1: Experiment Design
-**Required inputs:** hypothesis, primary metric, current baseline, minimum 
-detectable effect (MDE), available sample size per day.
-
-**Output:**
-- Hypothesis restated as: "If we [change], we expect [metric] to [move by X%] 
-  because [reason]"
-- Control and variant definitions
-- Primary metric (one only)
-- Secondary guardrail metrics (2-3 max)
-- Required sample size (calculated from MDE and baseline)
-- Estimated run time in days
-- Pre-defined success criteria (before the test runs — no moving goalposts)
-- Design risk flags: novelty effects, seasonal confounds, multiple testing issues,
-  network effects, sample ratio mismatch risks
+1. Restate hypothesis as: "If we [change], we expect [metric] to [move by X%] because [reason]"
+2. Define control and variant clearly
+3. Select primary metric (one only) and secondary guardrail metrics (2-3 max)
+4. Calculate required sample size from MDE and baseline
+5. Estimate run time in days
+6. Set pre-defined success criteria before the test runs — no moving goalposts
+7. Flag design risks: novelty effects, seasonal confounds, multiple testing issues, network effects, sample ratio mismatch
 
 ### Phase 2: Results Interpretation
-**Required inputs:** control results, variant results, p-value or raw numbers, 
-run duration, any anomalies observed.
+1. Assess statistical significance (p < 0.05 threshold)
+2. Assess practical significance: was the lift meaningful for the business, not just real?
+3. Interpret confidence intervals
+4. Investigate confounding factors
+5. Recommend: Ship / Iterate / Kill / Run follow-up test
+6. **Validate** — Confirm the test ran for the full planned duration. Flag if it was stopped early (peeking problem). Confirm sample ratio mismatch did not occur.
 
-**Output:**
-- Statistical significance assessment (p < 0.05 threshold)
-- Practical significance: was the lift meaningful for the business, not just real?
-- Confidence interval interpretation
-- Confounding factors to investigate
-- Recommendation: Ship / Iterate / Kill / Run follow-up test
-- If "Iterate": specific hypotheses to test next
+## Output Structure
+
+**[Design or Results header based on phase]**
+
+*Hypothesis:* "If we [change], we expect [metric] to [move by X%] because [reason]"
+
+*Primary metric:* [One metric only]
+*Guardrail metrics:* [2-3 max]
+*Required sample size:* [n per variant]
+*Estimated run time:* [days]
+*Pre-defined success threshold:* [specific number]
+*Design risk flags:* [any concerns]
+
+**Results (Phase 2 only):**
+*Statistical significance:* [p-value and conclusion]
+*Practical significance:* [lift size vs. business threshold]
+*Recommendation:* Ship / Iterate / Kill / Follow-up — [rationale]
 
 ## Quality Checks
-- Never interpret results from an underpowered test without flagging it
-- Always distinguish statistical from practical significance
-- Flag if test was stopped early (peeking problem)
-- Note if sample ratio mismatch occurred
+
+- [ ] Hypothesis specifies the change, the metric, the direction, and the reason
+- [ ] Primary metric is singular — guardrail metrics are secondary
+- [ ] Success criteria are defined before the test launches (not after seeing results)
+- [ ] Test was not stopped early (or flagged clearly if it was)
+- [ ] Practical significance assessed separately from statistical significance
+- [ ] Sample ratio mismatch is checked in results interpretation