brainstorm-okrs

name	brainstorm-okrs
description	OKR brainstorming and validation using the Radical Focus framework. Generates outcome-focused objectives with measurable key results and counter-metrics. Use when setting quarterly OKRs, validating existing OKRs against quality criteria, aligning team goals to company objectives, or teaching teams the difference between outputs and outcomes in goal-setting.
license	MIT + Commons Clause
metadata	{"version":"1.0.0","author":"borghei","category":"project-management","domain":"pm-execution","updated":"2026-03-04T00:00:00.000Z","python-tools":"okr_validator.py","tech-stack":"okr, radical-focus, goal-setting, strategic-planning"}

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