Meta review

Meta-Review v2.0.0

The quality control layer. Every skill output gets scored against clarity + feasibility standards, compared to tier benchmarks, trended over time, and validated against automated gates. Unique to v2.0.0: Pawel Huryn's positioning rigor (unique value clarity test) + 2026 GTM adoption learnings (cross-functional handoffs, enablement timing, propensity-based segmentation).

New in v2.0.0:

• Auto-fix mode (scoring + recommendation generation)
• Trending dashboard (quality metrics over 30/90 days)
• CI/CD integration (automated quality gates for launches)
• Tier-specific checklists (T1/T2/T3/T4 quality standards)
• Pawel positioning rigor (unique value clarity test on every positioning output)
• 2026 GTM adoption lens (adoption curve assessment, enablement timing validation, cross-functional handoff clarity)
• Persistent learning logging (team-wide quality insights compound across all users)

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Trigger

Auto-Trigger Mode (Primary):

• After any PMM skill session with output (brief, strategy, deck, etc.)
• Detects: skill name, output type, quality-relevant signals
• Does NOT trigger mid-session or before output complete

Manual Trigger (Secondary):

• User says: "review this", "quality check", "is this T1-ready?"
• User provides: skill output (document, brief, decision)

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Inputs

Auto-Trigger Detection:

• Skill name (extracted from execution context)
• Output type (brief, strategy, positioning, etc.)
• Output content (document, text, decisions)
• Launch tier (if applicable: T1/T2/T3/T4)
• Timestamp (for trending)

Context Files (Pre-Flight Load):

• /config/quality-scoring.yml — point system, rubrics (with Pawel positioning rigor)
• /config/tier-checklists.yml — tier-specific standards (T1-T4)
• /config/ci-gates.yml — CI/CD integration rules
• /config/adoption-lens.yml — 2026 GTM adoption curve framework
• /context/quality-trends.md — historical quality metrics
• /context/meta-patterns.md — guardrails + alerts
• /sessions/quality-learnings.md — persistent team-wide quality insights (NEW)

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Pre-Flight (Step 0)

1. Load config files (quality-scoring.yml, tier-checklists.yml, ci-gates.yml, adoption-lens.yml)
2. Detect output type (brief, strategy, positioning, retro, etc.)
3. Detect launch tier (T1/T2/T3/T4 or N/A if not applicable)
4. Load historical trends (context/quality-trends.md for comparison)
5. Load tier checklist (config/tier-checklists.yml for applicable tier)
6. Load team learnings (sessions/quality-learnings.md — what has the team learned about quality in this skill?)
7. Gate pass: If no output provided, ask user for it

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Steps

Step 1: Detect Output Type & Tier (30 sec)

What: Identify what kind of output this is and what tier (if applicable).

How:

• Scan skill name + output content for type clues
• Types: GTM brief, positioning statement, battlecard, retro summary, OKR doc, etc.
• Tier: Extract from skill name or ask user if unclear (T1/T2/T3/T4 or N/A)
• If tier unclear: default to "review for all tiers" (use most demanding checklist)

Output:

Detected:
  Skill: go-to-market-strategy
  Output type: GTM brief
  Tier: T2 (from skill output metadata)
  Quality audit scope: T2 standards + cross-tier checks

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Step 2: Load Quality Rubric + Team Learnings (1 min)

What: Load the scoring rubric for this output type + prior team learnings on this skill.

How:

1. Match output type to scoring rubric (e.g., "GTM brief" → gtm-brief-rubric)
2. Load point system (e.g., 100-point scale)
3. Load scoring dimensions (informed by Pawel rigor + 2026 GTM lens)
4. NEW: Load team learnings for this skill from quality-learnings.md
   • What has worked well? (e.g., "GTM briefs score higher when regional prioritization included")
   • What gaps keep appearing? (e.g., "Positioning outputs miss unique value angle")
   • What should I watch for? (guardrails from prior reviews)

Output:

Quality Rubric Loaded: GTM-Brief (100 points)
Dimensions:
  - Positioning clarity & unique value (Pawel rigor) (20 pts max)
  - Adoption curve & enablement timing (2026 lens) (20 pts max)
  - Cross-functional alignment (2026 lens) (20 pts max)
  - Success metrics (20 pts max)
  - Risk mitigation (10 pts max)
  - Timeline feasibility (10 pts max)

Team Learnings (GTM Brief Skill, Past 90 days):
  ✅ Strength: Regional prioritization in channel plan → +2 pts avg
  ⚠️ Gap: Enablement timeline often vague (when do reps get trained?) → -1.5 pts avg
  ⚠️ Gap: Sales handoff unclear (who owns ramp? CS or sales?) → missing from 40% of briefs

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Step 3: Score Output Against Rubric (3 min)

What: Grade the output point-by-point against the rubric.

How:

1. For each dimension, read output content
2. Compare to rubric criteria: is positioning clear (5 pts) / somewhat clear (3 pts) / vague (0 pts)?
3. NEW Pawel test (positioning outputs only): Does positioning own a unique value angle the incumbent can't claim?
   • YES (clear, bold, defensible): 20/20 pts
   • SOMEWHAT (clear but iterative): 15/20 pts
   • NO (features + incumbent framing): 5/20 pts
4. NEW adoption lens (GTM/launch outputs): Is enablement timing explicit and realistic?
   • YES (reps trained by launch day, timeline mapped): +2 pts bonus
   • VAGUE (training mentioned but no dates): -1 pt penalty
5. Assign points honestly (don't inflate)
6. Note evidence for each score (quotes or examples)
7. Track: total points, % complete, strengths, gaps

Output format:

Quality Score: GTM Brief

Dimension 1: Positioning Clarity & Unique Value (Pawel Rigor) (20 pts max)
  Score: 14/20 (70%)
  Evidence: "Positioning statement is clear vs. competitor X. However, feels iterative
            (faster + cheaper). Missing unique value angle only we can own."
  Pawel test: ❌ FAIL (doesn't own unique angle, incumbent could claim same positioning)
  Flagged: 🔴 CRITICAL (unique value test failed)

Dimension 2: Adoption Curve & Enablement Timing (2026 Lens) (20 pts max)
  Score: 16/20 (80%)
  Evidence: "Brief includes 'sales enablement' but no timeline. When do reps get trained?
            By launch day or weeks after? Unclear. CS handoff timing missing."
  2026 lens: ⚠️ VAGUE (enablement timing not explicit, -1 pt penalty applied)
  Flagged: 🟡 MEDIUM (enablement timing needs specificity)

Dimension 3: Cross-Functional Alignment (2026 Lens) (20 pts max)
  Score: 18/20 (90%)
  Evidence: "Brief clearly states: Sales owns ramp-up for first 30 days, CS takes over
            at 35 days. Marketing supports launch week. Clean handoff."
  2026 lens: ✅ PASS (cross-functional owners and handoff dates clear)

[... all dimensions ...]

TOTAL: 78/100 (78%)
Grade: B- (Acceptable for T2, below T1 standards)

Team Learnings Applied:
  ✅ Regional prioritization: Present (+2 pts noted)
  ⚠️ Enablement timing: Vague (-1 pt deducted per team learning)
  ✅ Sales/CS handoff: Clear (no deduction)

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Step 4: Compare to Tier Standards (2 min)

What: Check if output meets tier-specific quality gates.

How:

1. Load tier checklist from config/tier-checklists.yml (e.g., T2 standard = 75+ points)
2. Compare actual score (78) to tier minimum (75)
3. Identify gaps: does output pass T2? Would it pass T1? Would it fail T3?
4. Flag mismatches (e.g., "This is T2 output, but was scoped as T3")

Output:

Tier Comparison:

Output Score: 78/100 (78%)

T1 Standard: 90+ points required
  Status: ❌ BELOW (78 < 90)
  Gap: 12 points (missing unique value angle, vague enablement timing)

T2 Standard: 75+ points required
  Status: ✅ PASSES (78 > 75)
  Confidence: MEDIUM (only 3 points above threshold)

Recommendation: Output is T2-appropriate. Would need ~12 more points (unique value
positioning + explicit enablement timeline) to be T1-ready.

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Step 5: Generate Fix Recommendations (2 min)

What: For each gap identified in scoring, suggest specific improvements.

How:

1. For each dimension that scored <90%: generate fix
2. Fix must be: specific (not vague), actionable (user can do it), evidence-based (tied to scoring)
3. Prioritize fixes by impact + team learning insights
4. Surface: quick wins (5 min) vs. deep work (1+ hour)

Output:

Auto-Fix Recommendations (Priority Order, with Team Learning Insights)

FIX 1: Own Unique Value Angle (Pawel Rigor) [CRITICAL]
  Current: "Faster and cheaper than incumbent"
  Problem: Incremental. Incumbent could claim same thing. Fails Pawel unique value test.
  Team insight: "50% of positioning outputs miss unique angle. Those that own one score +5 pts avg."
  
  Suggested fix: Identify one value dimension incumbent doesn't own (e.g., vertical focus,
               business model, implementation approach) and reframe:
               "Only platform purpose-built for [vertical], enabling [outcome] in [timeframe]"
  Estimated impact: +6 points (positioning → 20/20)
  Effort: 30 min
  
FIX 2: Make Enablement Timing Explicit (2026 Lens) [HIGH IMPACT]
  Current: "Sales enablement TBD"
  Problem: Vague. When do reps train? Before or after launch? Unclear.
  Team insight: "Enablement timing specificity matters. Briefs with explicit training
                dates on launch timeline score +1.5 pts avg."
  
  Suggested fix: Add: "Sales training: [date], delivered via [format]. CS begins
                handoff: [date]. Post-launch monitoring window: [days]"
  Estimated impact: +2 points
  Effort: 15 min

Quick Wins Summary: 2 fixes, 45 min total effort, +8 points (78 → 86, enters T1 range)

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Step 6: Adoption Curve Assessment (1 min)

What: For GTM/launch outputs: assess if adoption curve is realistic and enablement timing is right.

How:

1. Load adoption-lens.yml (2026 framework: sales ramp curve, CS handoff window, expansion trigger)
2. Check: Does brief account for sales ramp-up time (typically 30-60 days)?
3. Check: Does brief specify CS handoff (when does customer success take primary ownership)?
4. Check: Are expansion triggers defined (when do we try to upsell)?
5. Flag: If enablement happens post-launch (risky), if handoff is undefined, if expansion triggers missing

Output:

Adoption Curve Assessment (2026 GTM Lens)

Sales Ramp-Up:
  Brief states: "Sales ramp 30 days"
  Assessment: ✅ REALISTIC (30 days is standard for T2)
  Note: Brief includes training timeline + success criteria for ramp completion

CS Handoff:
  Brief states: "CS takes primary ownership at day 35"
  Assessment: ✅ CLEAR (handoff date is explicit)
  Note: Sales/CS handoff is clean, no overlap or gap

Expansion Triggers:
  Brief states: "Expansion upsell kicks in at 60 days (post-stability window)"
  Assessment: ✅ DEFINED (expansion is intentional, not reactive)

Overall: ✅ ADOPTION CURVE IS REALISTIC (brief accounts for standard sales ramp + handoff)

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Step 7: Trend Analysis (1 min)

What: Compare this output's score to historical quality trends.

How:

1. Load context/quality-trends.md (scores from past 30/90 days)
2. Calculate: average score, trend direction (improving/stable/declining), volatility
3. Load team learnings: what does the team know about this skill's quality trajectory?
4. Surface: "Your GTM briefs are improving 2 points/month" or "This is your highest-scoring positioning yet"

Output:

Quality Trends (GTM Brief Skill, Past 90 Days)

Historical Scores:
  2026-05-21: 71/100 (First brief, learning phase)
  2026-06-04: 74/100 (+3 points)
  2026-06-18: 76/100 (+2 points)
  2026-07-01: 77/100 (+1 point)
  2026-07-21: 78/100 (today, +1 point)

Trend Analysis:
  Average (90 days): 75.2/100
  Today's score: 78/100 (+3.8 points above average)
  Trend: ↗️ IMPROVING (avg +1.5 pts/month)
  
Team Learning: "GTM briefs improve fastest when team focuses on (1) unique positioning,
               (2) explicit enablement timing. Those two levers account for +4 pts avg improvement."

Insight: "Your GTM briefs are improving steadily. This one is your best yet. Keep pushing
         on unique value angle — that's your highest-impact lever for next 5-10 points."

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Step 8: Predict Tier Success (1 min)

What: Estimate confidence: will this output succeed at its intended tier?

How:

1. Use historical data: "GTM briefs scoring 78 have succeeded X% of the time at T2"
2. Factor in: output confidence + guardrails loaded + execution quality + team calibration
3. Confidence band: Low (0-40%), Medium (40-70%), High (70-90%), Very High (90%+)
4. Flag risks: if confidence is low, what needs to improve?

Output:

Tier Success Prediction: T2 Launch

Confidence: 72% (MEDIUM-HIGH)

Reasoning:
  Output quality: 78/100 (meets T2 minimum)
  Historical data: T2 briefs scoring 75-80 succeed 70% of the time
  Team calibration: Team has improved by +3.8 pts on average (confidence improving)
  Guardrails loaded: Positioning clarity rule active (medium-high impact)
  Execution risk: Medium (enablement timing vague, could slip)

Success Probability: 72% (will hit T2 targets) ± 12%
  Low end: 60% (if enablement delayed, unique positioning doesn't land)
  High end: 84% (if enablement sharp, positioning resonates)

Risk Factors:
  🔴 Positioning still incremental (may not land in market)
  🟡 Enablement timing vague (could slip post-launch)
  🟢 CS/sales handoff is clear (will know quickly if working)

Recommendation:
  → Proceed with T2 launch (confidence is solid)
  → Use Fix 1 & 2 to improve confidence to 80%+
  → Monitor enablement ramp (early signal of success/failure)

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Step 9: CI/CD Gate Decision (1 min)

What: Should this output pass automated quality gates (e.g., for CI/CD pipeline)?

How:

1. Load config/ci-gates.yml (gate rules by skill + tier)
2. Check: does output meet gate criteria?
   • Gate 1: Score ≥ tier minimum? (78 ≥ 75 for T2)
   • Gate 2: No critical gaps? (none marked 🔴 critical here)
   • Gate 3: Trend acceptable? (improving or stable, not declining)
3. If all gates pass: output clears CI/CD (can deploy)
4. If any gate fails: output blocked (needs fixes before deploy)

Output:

CI/CD Gate Evaluation (T2 GTM Brief)

Gate 1: Quality Score Threshold
  Required: ≥75 for T2
  Actual: 78
  Status: ✅ PASS

Gate 2: No Critical Gaps
  Critical issues found: 1 (positioning unique value test failed)
  Blocking issues: 0 (critical is flagged but not blocking, fixable)
  Status: ⚠️ CONDITIONAL (Fix 1 required, then resubmit)

Gate 3: Quality Trend Acceptable
  Trend: Improving (+1.5 pts/month)
  Volatility: Low (stable)
  Status: ✅ PASS

Gate 4: Guardrail Checks
  Active guardrails: 2 (unique value clarity, enablement timing)
  Violations: 1 (unique value clarity hit)
  Status: ⚠️ CONDITIONAL (same as Gate 2)

Overall: ⚠️ CONDITIONAL APPROVAL

Decision: OUTPUT NEEDS FIX #1 BEFORE DEPLOYMENT
  Critical gap: Positioning doesn't own unique value angle (Pawel test failed)
  Fix: Reframe positioning to own unique dimension (est. 30 min)
  Next: Resubmit for re-scoring, should clear gates with +6 point boost

Or: Skip fix, deploy as-is (if timeline pressure warrants risk)

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Step 10: Log Learnings for Team (1 min)

What: Record quality insights from this review that help future team members improve.

How:

1. Append to /sessions/quality-learnings.md (persistent team knowledge)
2. Format: skill, output type, what worked, what didn't, guardrail hit (if any)
3. These learnings feed back into Step 2 for future reviews (team knowledge compounds)

Output (logged to quality-learnings.md):

## 2026-07-21 | GTM Brief Quality Review

Skill: go-to-market-strategy
Output Type: GTM brief
Tier: T2
Quality Score: 78/100
Outcome: CONDITIONAL (needs unique value positioning fix)

What Worked Well:
  ✅ Sales/CS handoff was crystal clear (date + handoff owner specified)
  ✅ Regional prioritization included (leveraging team learning from prior reviews)
  ✅ Cross-functional alignment clear (no ambiguity on who owns what)

What Didn't Work:
  ❌ Positioning felt incremental (incumbent could claim same "faster + cheaper")
  ⚠️ Enablement timeline vague (when exactly do reps train? No date)

Guardrail Hit:
  🔴 CRITICAL: Positioning unique value test (Pawel rigor) FAILED
     Rule: Positioning must own unique value angle incumbent doesn't
     Why: "Faster + cheaper" is not unique to us
     Fix: Identify one value dimension we own (vertical focus, implementation model, etc.)
          and reframe around that unique angle

Leverage for Next Team Member:
  → When reviewing GTM briefs, run the Pawel unique value test EARLY
  → If test fails, it's a show-stopper (Pawel rigor score can drop 6+ points)
  → Explicit enablement dates matter (affects adoption curve assessment)
  → Regional prioritization + clear sales/CS handoff are key strengths to build on

Confidence if Fixed: 80%+ (would move into T1 territory)

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Step 11: Close & Recommend Next Action (1 min)

What: Summarize everything, show what changed, suggest next step.

How:

1. Recap: quality score + tier match + confidence + gates + learnings logged
2. Recommend: proceed, fix + retry, or escalate
3. Link to meta-learn: "Quality insight logged, meta-learn will compound this data"
4. Link to team: "This learning is in quality-learnings.md for your team"

Output:

✓ Quality Review Complete

Summary:
  Quality Score: 78/100 (B-, acceptable for T2)
  Tier Match: ✅ CORRECT (T2 output, assigned T2)
  Pawel Positioning Test: ❌ FAILED (incremental, doesn't own unique angle)
  Adoption Curve: ✅ REALISTIC (sales/CS handoff clear, enablement timing vague)
  CI/CD Gates: ⚠️ CONDITIONAL (Fix #1 required, then resubmit)
  Success Confidence: 72% ± 12% (medium-high, improvable to 80%+)

Recommendation:
  → PRIMARY: Run Fix 1 (own unique positioning angle) in parallel to launch prep
  → SECONDARY: Run Fix 2 (explicit enablement dates) to sharpen execution
  → MONITOR: Track sales ramp curve vs. 30-day plan (early signal of success)
  → TEAM: This review's learnings logged to quality-learnings.md (helps team improve)

Quality logged to /sessions/quality-log.md (trends updated)
Team insights logged to /sessions/quality-learnings.md (team knowledge compounds)
Guardrail hit logged for meta-learn (unique value positioning pattern tracked)

Next step: Fix and resubmit, or proceed as-is with risk noted.

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Operating Rules

1. Pawel rigor is non-negotiable for positioning. Unique value clarity test is a gate, not optional.

2. 2026 adoption lens is required for launches. Sales ramp + CS handoff + expansion triggers must be explicit.

3. Team learnings compound. Every review adds insights that make next reviews sharper. Use them.

4. Score honestly, not generously. Quality control only works if scoring is rigorous.

5. Tier standards are minimums. 75 is barely passing. Aim for 80+.

6. Fixes are ranked by impact. Prioritize unique value clarity + enablement timing (highest-impact levers).

7. Enablement timing specificity matters. Vague enablement timelines correlate with launch delays.

8. Cross-functional handoffs prevent chaos. If sales/CS handoff is undefined, output fails adoption curve test.

9. Trends matter as much as snapshots. A 78 that's improving beats a 78 that's declining.

10. Learning is persistent. Team knowledge is logged. Use it to help each other improve.

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Quality Gate

Before finalizing any review, verify:

• Output type detected correctly
• Launch tier identified
• Quality rubric loaded (with Pawel rigor + adoption lens)
• All dimensions scored with evidence
• Pawel unique value test run (positioning outputs only)
• Adoption curve assessed (launch/GTM outputs only)
• Tier standards checked
• Fix recommendations specific + ranked by impact
• Team learnings applied (prior insights referenced)
• Confidence prediction explained
• CI/CD gates evaluated
• Learnings logged to quality-learnings.md
• Session logged to quality-log.md

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Related Files

• /config/quality-scoring.yml — Point system, rubrics (with Pawel positioning rigor, 2026 adoption lens)
• /config/tier-checklists.yml — Tier-specific quality standards (T1-T4)
• /config/ci-gates.yml — CI/CD gate rules
• /config/adoption-lens.yml — 2026 GTM adoption curve framework (NEW)
• /context/quality-trends.md — Historical quality metrics
• /context/meta-patterns.md — Active guardrails
• /sessions/quality-log.md — Quality score history
• /sessions/quality-learnings.md — Persistent team-wide quality insights (NEW, compounds across all users)

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Sources

The enhanced meta-review v2.0.0 integrates learnings from:

• Pawel Huryn - The Product Compass — Positioning rigor framework (unique value clarity test)
• Pawel Huryn - AI PM Learning Program — Decision-making quality standards
• 2026 GTM trends: Cross-functional alignment, adoption curve timing, enablement specificity
• Ramp GTM learnings: Sales ramp curves, CS handoff windows, expansion trigger design