OKR Framework Pack

We built this so you don't have to build a validator for your strategy from scratch. Most OKR implementations fail because they treat strategy like a document instead of a system. You can't cascade what you can't validate. This pack gives your agents and your team a machine-readable, schema-enforced framework for setting objectives, mapping alignment, and running reviews that actually surface the truth.

If you're an engineer who has watched strategy drift into vague wish lists, you know the pain. You see the misalignment. You see the orphaned objectives. You see the quarterly reviews that are just blame games because there's no baseline to compare against. We built the OKR Framework Pack to bring the same rigor you apply to your codebase to your OKRs. It's not a guide. It's a validation layer.

The "Team Goal" Trap

Most teams treat OKRs like a wish list. You open a doc, type "Improve customer satisfaction," and call it a day. It's garbage. Without a strict schema, objectives drift into vague aspirations and key results become arbitrary numbers. We've seen this in every org we've audited. The framework breaks because it lacks structural integrity. You need to treat OKRs like code. If you can't validate the structure, you can't trust the strategy.

This is why we built the OKR Framework Pack. It's not a guide; it's a validation layer for your strategy. It forces the distinction between qualitative objectives and numeric key results. It prevents the common anti-pattern of mixing tasks with outcomes. If you're currently using a generic Goal Setting & Tracking Pack and finding that your goals are too vague, this pack adds the structural rigor you're missing.

HBR emphasizes that OKRs should be set for teams, not individuals ^[1]. When you blur that line, you get gaming the system. Our framework enforces team-level objectives with clear alignment paths. You don't get to submit an OKR without an owner and an alignment_id. The schema catches the drift before it hits your quarterly review.

Why Drafting OKRs in a Doc Costs You P95 Velocity

When OKRs are unstructured, the cost isn't just wasted time. It's misalignment. If Engineering's KR doesn't mathematically link to the Company Objective, you're building features that don't move the needle. McKinsey notes that OKRs provide a tight link to dynamic funding and talent allocation ^[6]. Without that link, you're burning runway.

Also, cascading is where it dies. If you don't have a traceable map from Company to Individual, you get siloed goal-setting ^[7]. You end up with "shadow OKRs" in Slack threads and spreadsheets that nobody trusts. The quarterly review becomes a blame game because there's no baseline to compare against. You lose the ability to detect alignment drift until the quarter is over.

We built the cascade-alignment.csv to solve this. It's not a doc; it's a tabular mapping template. It includes columns for parent_id, child_id, alignment_score, dependency_type, and review_cycle. This enables traceability and prevents flat OKR anti-patterns. If you're struggling with cross-functional dependencies, this pack integrates with your Growth Strategy Pack to ensure your growth loops are actually aligned with your OKRs.

Without this, your Internal Communications Pack is just broadcasting noise. If the team doesn't know how their work links to the company vision, your town halls are just status updates. You need a system that enforces the link.

A SaaS Company's Q3 Alignment Disaster

Imagine a team that launches a new OKR cycle. The VP of Product sets a company objective: "Dominate the SMB market." Engineering sets a KR: "Ship API v2." Product sets a KR: "Launch onboarding flow."

But there's no alignment ID linking them. Engineering's KR has no weight. The sum of weights across the department is 1.2, not 1.0. The quarterly review happens. The VP asks, "Did we hit the target?" The PM says, "We shipped v2." The VP asks, "Did it impact SMB acquisition?" The PM has no data. The OKR was a task, not an outcome. This is the anti-pattern. HBR warns against mixing tasks with outcomes and over-scoping ^[3].

Without a schema to reject this, the org continues to drift. The examples/production-okrs.yaml in this pack shows what a real Q3 set looks like. It has a company objective with 4 KRs, department objectives with weighted KRs, team initiatives, and alignment IDs. It demonstrates proper numeric targets, baseline tracking, and cross-team dependencies.

If you're considering a Pivot Strategy Pack, you need OKRs that are flexible enough to change but rigid enough to measure. This pack gives you that. The scripts/validate_okrs.py script loads your YAML, validates against the schema, cross-references alignment, and exits 1 on failure. You don't get to deploy bad data.

This pack also pairs with a Business Model Canvas Pack to ensure your strategic assumptions are tested against your OKR outcomes. If your canvas assumptions are wrong, your OKRs will show it in the variance analysis.

What Changes Once the Schema Is Locked

With the pack installed, the agent enforces the rules. You define the objective in YAML. The schema checks the fields. It ensures type is valid. It checks that key_results have baseline, target, and current. It validates that weights sum to 1.0. It cross-references alignment_ids against the cascade map.

If you try to submit a malformed OKR, scripts/validate_okrs.py exits with code 1. You get a clear error. The alignment map is a CSV with parent_id, child_id, and alignment_score. You can trace every team goal back to the company vision.

The quarterly review template forces a variance analysis. You don't guess; you measure. The templates/quarterly-review.md includes sections for objective attainment %, key_result variance analysis, initiative impact assessment, alignment drift detection, and next-quarter adjustments. This is grounded in standard OKR cadence practices.

For remote teams, this structure is critical. A Remote Team Management Pack relies on async clarity. If your OKRs are clear and validated, your async updates are focused. You don't need a Meeting Management Pack to debate whether a goal was met. The data is in the schema.

We also include references/okr-canon.md and references/cascading-alignment.md. These are embedded knowledge bases. They define the rules: qualitative objectives, numeric KRs, scoring methodology (0.0-1.0), and conflict resolution protocols. You don't need to memorize the framework; the agent has it.

The examples/cascade-map.json is a machine-readable alignment map. It links company OKRs to department and team OKRs. It includes alignment scores, dependency flags, and review timestamps. You can use this to verify hierarchical coherence and detect orphaned or misaligned objectives.

The tests/okrs-validate.test.sh is a bash test harness. It runs the validator against the production example (expects exit 0) and a deliberately malformed draft (expects exit 1). This validates that the framework enforces structural integrity before deployment. You can run this in CI/CD if you want to treat your strategy like code.

What's in the OKR Framework Pack

• skill.md — Orchestrator skill that defines the OKR implementation workflow, references all relative paths (templates/, references/, validators/, scripts/, examples/, tests/), and instructs the agent on how to cascade objectives, validate alignment, run quarterly reviews, and enforce measurable outcomes.
• templates/okr-definition.yaml — Production-grade OKR template with strict fields: id, type (company/department/team/individual), quarter, year, objective, key_results (baseline, target, current, weight, metrics), initiatives, alignment_ids, owner, and status. Enforces numeric targets and dependency tracking per Atlassian/Community best practices.
• templates/cascade-alignment.csv — Tabular mapping template for hierarchical alignment (Company → Department → Team → Individual). Includes columns for parent_id, child_id, alignment_score, dependency_type, and review_cycle. Enables traceability and prevents flat OKR anti-patterns.
• templates/quarterly-review.md — Structured review template for end-of-quarter retrospectives. Includes sections for objective attainment %, key_result variance analysis, initiative impact assessment, alignment drift detection, and next-quarter adjustments. Grounded in standard OKR cadence practices.
• references/okr-canon.md — Embedded canonical knowledge: OKR vs KPI distinctions, objective formulation rules (qualitative, inspiring, time-bound), key result design (measurable, ambitious, binary/numeric), cadence (quarterly), scoring methodology (0.0-1.0), and common pitfalls (over-scoping, mixing tasks with outcomes).
• references/cascading-alignment.md — Embedded guidance on hierarchical OKR mapping: top-down vision alignment, bottom-up feasibility validation, cross-functional dependency resolution, alignment scoring (0-100%), and conflict resolution protocols. Prevents siloed goal-setting and ensures strategic coherence.
• validators/okr-schema.json — JSON Schema enforcing strict OKR structure: required fields, type constraints, numeric validation for key_result targets/baselines/current, weight summation rules (must equal 1.0), alignment_id format, and quarter/year format. Used by the validator script to reject malformed inputs.
• scripts/validate_okrs.py — Executable Python validator that loads templates/okr-definition.yaml, validates against validators/okr-schema.json, cross-references alignment_ids with templates/cascade-alignment.csv, verifies numeric targets, checks weight sums, and exits 1 on any schema/alignment failure. Implements real runnable workflow.
• examples/production-okrs.yaml — Realistic Q3 production OKR set for a SaaS company: Company objective with 4 KRs, Department objectives with weighted KRs, team initiatives, and alignment IDs. Demonstrates proper numeric targets, baseline tracking, and cross-team dependencies.
• examples/cascade-map.json — Machine-readable alignment map linking company OKRs to department and team OKRs. Includes alignment scores, dependency flags, and review timestamps. Used to verify hierarchical coherence and detect orphaned or misaligned objectives.
• tests/okrs-validate.test.sh — Bash test harness that runs scripts/validate_okrs.py against examples/production-okrs.yaml (expects exit 0) and a deliberately malformed draft (expects exit 1). Validates that the OKR framework enforces structural integrity and alignment rules before deployment.

Install and Ship

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