# Annual Operating Plan > Reconciles leadership's top-down target against the driver-based bottom-up build line by line via a bundled calculator, flagging every gap over 10% instead of quietly averaging the two into a number nobody actually believes. Use whenever the user is building the annual operating plan, needs to reconcile a leadership target with a bottoms-up model, or has a plan where the top-down ask and the bott… - **Type:** Skill - **Install:** `agentstack add skill-natan-mohart-24-finance-skills-for-claude-annual-operating-plan` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [Natan-Mohart](https://agentstack.voostack.com/s/natan-mohart) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [Natan-Mohart](https://github.com/Natan-Mohart) - **Source:** https://github.com/Natan-Mohart/24-finance-skills-for-claude/tree/main/skills/annual-operating-plan ## Install ```sh agentstack add skill-natan-mohart-24-finance-skills-for-claude-annual-operating-plan ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Annual Operating Plan ## When to use Use whenever the annual plan needs to reconcile what leadership wants (a top-down growth or margin target, often set for investor or board reasons) against what a driver-based bottom-up model actually produces. Especially use when the current "plan" is really just the top-down number with a supporting narrative built backward to justify it, rather than a genuine reconciliation. ## What it does Compares top-down target and bottom-up build line by line via a bundled calculator, computes the gap and gap percentage for every line item, and flags any line with more than a 10% gap — forcing an explicit resolution (revise the target, find real drivers to close the gap, or accept it with a named owner) instead of letting the two numbers get quietly blended into a plan nobody actually built. ## Method 1. **Build the bottom-up plan first, independently.** Use driver-based-budget-builder's discipline: real volume, price, and cost drivers by department, not a top-down number divided by twelve. 2. **State the top-down target explicitly** — the number leadership or the board has in mind, and why (a growth rate expected by investors, a margin target tied to a prior commitment). 3. **Run the bundled calculator** (`scripts/aop_reconciliation.py`) to compare every line item, revenue, margin, opex, and any other planned metric, top-down versus bottom-up, with the gap and gap percentage shown. 4. **Treat every flagged gap (over 10%) as requiring one of three resolutions**, not a rounding error to smooth over: revise the top-down target to something the bottom-up model can actually support, find specific additional drivers (a new channel, a price change, a cost action) that would close the gap for real, or explicitly accept the gap with a named owner and a plan to close it during the year. 5. **Never let the published plan silently split the difference** between top-down and bottom-up without documenting which resolution was chosen and why — a plan that's an unexplained average of two numbers is not a real plan, it's a compromise nobody can defend later. 6. **Revisit the reconciliation at each planning checkpoint** (quarterly re-forecast), not just once at the start of the year — the gap-closing actions from step 4 need to actually show up in the numbers, or the same gap reappears next cycle. ## Inputs - Top-down targets by line item with the rationale behind each - Bottom-up, driver-based build for the same line items - Config saved as JSON matching the format documented at the top of `scripts/aop_reconciliation.py` ## Output format Line-by-line table of top-down target, bottom-up build, gap, and gap percentage; explicit flag for every line item over a 10% gap; required resolution stated for each flagged item (revised target, closing drivers, or accepted gap with owner). ## Example Leadership's top-down revenue target is $24M; the bottom-up build, based on realistic sales capacity and conversion rates, comes to $21.5M, a 10.4% gap the calculator flags immediately. Rather than publishing $24M with a hopeful narrative, the resolution names two specific closing drivers (a new outbound motion and a price increase on renewals) sized to close roughly half the gap, with the remaining gap explicitly accepted and owned by the sales VP with a Q2 checkpoint. ## Common pitfalls - Publishing the top-down number as the plan without ever showing the bottom-up build that was supposed to support it. - Averaging top-down and bottom-up into a compromise number with no stated rationale for the blend. - Reconciling once at the start of the year and never revisiting whether the gap-closing actions actually materialized. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [Natan-Mohart](https://github.com/Natan-Mohart) - **Source:** [Natan-Mohart/24-finance-skills-for-claude](https://github.com/Natan-Mohart/24-finance-skills-for-claude) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-natan-mohart-24-finance-skills-for-claude-annual-operating-plan - Seller: https://agentstack.voostack.com/s/natan-mohart - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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