OKR Brainstorming Expert

The agent generates and validates outcome-focused OKR sets using Christina Wodtke's Radical Focus methodology. It produces inspirational objectives with measurable key results, applies counter-metric tests, and scores quality against proven criteria.

Workflow

1. Identify the Theme

The agent asks: "What is the single most important thing this team needs to change this quarter?" The answer becomes the theme. Every OKR must connect back to this theme.

Validation checkpoint: If the user provides more than one theme, the agent pushes back. One theme per team per quarter. Multiple themes means no focus.

2. Generate 3 Distinct OKR Sets

For each set, the agent produces:

1. Objective -- One qualitative, inspirational statement (no numbers)
2. Key Result 1 -- Primary metric proving progress
3. Key Result 2 -- Secondary metric capturing a different dimension
4. Key Result 3 -- Counter-metric preventing gaming of KR1 and KR2
5. Rationale -- 2-3 sentences on why this set matters and how it connects to the theme

Objective quality criteria:

• Qualitative (numbers belong in key results)
• Inspirational (team would be excited to achieve it)
• Time-bound (achievable within one quarter)
• Actionable (team can directly influence the outcome)

Key result quality criteria:

• Measurable (has a metric with a number)
• Outcome-focused (measures results, not activities)
• Set at 60-70% confidence (not sandbagging, not demoralizing)
• Limited to 3 per objective

3. Apply the Counter-Metric Test

For every pair of key results, the agent asks: "Could we hit these numbers by doing something harmful?" If yes, it adds a counter-metric.

Example: If KR1 is "Increase sign-ups by 40%", a counter-metric is "Maintain activation rate above 60%." Without it, the team could game KR1 by lowering sign-up barriers so far that unqualified users flood in.

4. Validate with Tool

python scripts/okr_validator.py --input okrs.json

The validator scores each OKR set and surfaces quality issues: disguised tasks, missing metrics, output-framed key results, or missing counter-metrics.

Validation checkpoint: Any OKR set scoring below 70% must be revised before committing.

Example: Quarterly OKR Generation

Input: Theme is "retention" for a SaaS product team.

Output:

OKR Set 1:
  Objective: "Become the product teams can't imagine leaving"
  KR1: Reduce monthly churn from 4.2% to 2.5%
  KR2: Increase 90-day retention cohort from 68% to 82%
  KR3 (counter): Maintain NPS score above 45 (prevent forced lock-in tactics)
  Rationale: Churn is the top revenue leak. Improving retention directly
  increases LTV and reduces pressure on acquisition spend.

OKR Set 2:
  Objective: "Make our onboarding so good that users hit value in their first session"
  KR1: Increase Day-1 activation rate from 34% to 55%
  KR2: Reduce time-to-first-value from 12 minutes to under 4 minutes
  KR3 (counter): Maintain support ticket volume below 200/week (don't hide complexity)
  Rationale: Users who activate on Day 1 retain at 3x the rate. Onboarding
  is the highest-leverage retention lever.

OKR Set 3:
  Objective: "Turn our power users into vocal advocates"
  KR1: Increase referral-sourced signups from 8% to 20% of new users
  KR2: Grow active community members from 500 to 2,000
  KR3 (counter): Maintain power user retention above 95% (don't distract them)
  Rationale: Advocacy compounds. Referred users have 37% higher retention
  than paid-acquisition users.

$ python scripts/okr_validator.py --input okrs.json

OKR Validation Results
======================
Set 1: 92/100 - PASS
  Objective: Qualitative, inspirational, time-bound
  KR1: Measurable, outcome-focused, stretch target
  KR2: Measurable, different dimension from KR1
  KR3: Valid counter-metric for churn reduction

Set 2: 88/100 - PASS
  Objective: Qualitative, inspirational, time-bound
  KR1: Measurable, outcome-focused
  KR2: Measurable, tracks different dimension
  KR3: Valid counter-metric
  Note: "under 4 minutes" - verify baseline measurement exists

Set 3: 85/100 - PASS
  Objective: Qualitative, inspirational
  KR1: Measurable, outcome-focused
  KR2: Measurable, but "active" needs precise definition
  KR3: Valid counter-metric

Common OKR Mistakes

| Mistake | Example | Fix | |---------|---------|-----| | Disguised task | "Launch the mobile app" | Ask "why?" -- measure the outcome the launch enables | | Too many OKRs | 5 objectives per team | Pick 1, maybe 2. More means no focus | | 100% confidence | Target you know you will hit | Stretch to 60-70% confidence | | Activity metric | "Publish 12 blog posts" | Measure impact: "Increase organic traffic by 30%" | | Set and forget | Review only at quarter end | Weekly check-ins with confidence scoring | | Top-down only | All OKRs from leadership | Combine top-down direction with bottom-up team insight |

OKRs vs KPIs vs North Star Metric

| Concept | Purpose | Cadence | Example | |---------|---------|---------|---------| | North Star Metric | Single metric capturing core value delivery | Permanent | Weekly active users completing a workflow | | KPIs | Health indicators across the business | Ongoing | Revenue, churn rate, response time | | OKRs | Ambitious quarterly goals that move KPIs | Quarterly | "Become the fastest onboarding in our category" |

Relationship: OKRs are the lever pulled to move KPIs toward the North Star Metric. KPIs indicate business health. The NSM indicates core value delivery. OKRs define what changes this quarter.

Tools

| Tool | Purpose | Command | |------|---------|---------| | okr_validator.py | Validate and score OKR sets | python scripts/okr_validator.py --input okrs.json | | okr_validator.py | Run demo validation | python scripts/okr_validator.py --demo |

Troubleshooting

| Symptom | Likely Cause | Resolution | |---------|-------------|------------| | OKR set scores below 70% consistently | Key results framed as tasks/outputs instead of outcomes, or objective contains numbers | Ask "So what?" for each KR until you reach a measurable outcome; remove numbers from objectives | | Validator flags "output-oriented language" | KR description starts with verbs like "launch", "build", "implement", "ship" | Reframe: "Launch mobile app" becomes "Increase mobile-originated revenue from 0% to 15%" | | Team sets 5+ objectives per quarter | Lack of strategic focus or inability to say no | Enforce 1 theme per team per quarter; use the Radical Focus constraint: one objective, maybe two | | Key results hit 100% every quarter | Targets are sandbagged at 100% confidence | Stretch to 60-70% confidence; if you hit every KR, you are not being ambitious enough | | Counter-metrics missing from OKR sets | Team did not apply the gaming test to KR pairs | For every pair of KRs, ask: "Could we hit these numbers by doing something harmful?" Add a counter-metric if yes | | OKRs set and forgotten until quarter end | No weekly check-in rhythm established | Implement weekly confidence scoring (red/yellow/green) per KR; teams with weekly check-ins complete 43% more goals | | Validator rejects input JSON | Schema mismatch: missing okr_sets key or key_results array per set | Ensure JSON has okr_sets array, each with objective string and key_results array containing description, metric, target_value, current_value |

Success Criteria

• Each OKR set scores above 80/100 on the validator before committing to the quarter
• Maximum 1-2 objectives per team per quarter (focus over breadth)
• Every objective is qualitative and inspirational (no numbers in the objective itself)
• Each objective has exactly 3 key results: primary metric, secondary dimension, and counter-metric
• Key results are set at 60-70% confidence (stretch, not sandbagged)
• Weekly confidence check-ins are conducted, not just end-of-quarter reviews
• OKR retrospectives run at quarter end with structured review of what was learned

Scope & Limitations

In Scope:

• OKR brainstorming using Christina Wodtke's Radical Focus methodology
• Generating 3 distinct OKR sets per theme with counter-metric testing
• Automated validation and scoring of OKR quality (output detection, metric presence, structural checks)
• Guidance on OKR vs. KPI vs. North Star Metric distinctions
• Common OKR mistake identification and remediation

Out of Scope:

• OKR tracking and progress monitoring over the quarter (use dedicated OKR platforms)
• Company-level OKR cascade and alignment across teams (see senior-pm/ for portfolio alignment)
• Individual performance-linked OKRs (OKRs should be team goals, not performance reviews)
• Metric instrumentation or analytics setup for measuring key results

Important Caveats:

• OKRs work best when combined with weekly check-ins. Teams that review OKRs only at quarter end see 30-45% lower completion rates.
• The validator catches structural issues but cannot assess strategic quality. A perfectly scored OKR can still be the wrong goal.
• OKRs should be aligned top-down (strategic direction) and bottom-up (team insight). Pure top-down OKRs reduce team ownership.

Integration Points

| Integration | Direction | Description | |------------|-----------|-------------| | scrum-master/ | Receives from | Sprint velocity and capacity data inform realistic KR target-setting | | senior-pm/ | Receives from | Portfolio strategic priorities shape quarterly OKR themes | | execution/outcome-roadmap/ | Feeds into | OKR key results become success metrics for roadmap Now/Next items | | execution/prioritization-frameworks/ | Complements | Prioritized initiatives inform which OKR theme to focus on | | discovery/identify-assumptions/ | Receives from | Validated assumptions increase confidence in OKR target feasibility | | discovery/brainstorm-experiments/ | Feeds into | Experiment metrics may become OKR key results when validated |

Tool Reference

okr_validator.py

Validates and scores OKR sets against quality criteria. Checks objectives for qualitative/inspirational language, key results for measurable outcomes, and structural completeness.

| Flag | Type | Default | Description | |------|------|---------|-------------| | --input | string | (required, mutually exclusive with --demo) | Path to JSON file containing OKR sets | | --demo | flag | off | Run validation on built-in demo data (mix of good and bad OKRs) | | --format | choice | text | Output format: text or json |

Input JSON schema:

{
  "okr_sets": [
    {
      "objective": "string (qualitative, no numbers)",
      "key_results": [
        {
          "description": "string",
          "metric": "string (unit of measurement)",
          "target_value": "number",
          "current_value": "number (baseline)"
        }
      ]
    }
  ]
}

References

• references/okr-best-practices.md -- Detailed OKR guide with examples and anti-patterns
• assets/okr_template.md -- OKR document template and quarterly review format