capital-allocation
Evaluates where to spend limited capital — investment appraisal, hurdle rates, payback, and comparing proposals that are not alike. Use this to evaluate an investment or major purchase, compare competing funding requests, set a hurdle rate, decide between building and buying, or review whether past investments delivered what was claimed.
git clone --depth 1 https://github.com/cbrock84/headcount /tmp/capital-allocation && cp -r /tmp/capital-allocation/plugins/finance/skills/capital-allocation ~/.claude/skills/capital-allocationSKILL.md
# Capital allocation Choosing among investments is choosing what not to do. This is the appraisal of individual proposals; which businesses and bets the company should be in belongs to `corporate-strategy:portfolio-strategy`. ## Appraise on incremental cash Only cash flows that change because of the decision belong in the analysis: - **Sunk costs are irrelevant.** Money already spent is not a reason to continue, though it is reliably presented as one. - **Allocated overhead is usually irrelevant.** If the cost occurs anyway, it does not belong in the incremental case. - **Opportunity cost is relevant**, including the capacity consumed that then cannot serve anything else. - **Working capital is a real outflow.** Growth that consumes cash is not free because it is growth. Discount for time and risk. A hurdle rate should reflect the risk of the specific proposal — applying one company-wide rate systematically overfunds risky projects and starves safe ones. ## Read payback for what it tells you Payback ignores everything after the threshold and so is a poor ranking tool. It is a good *liquidity and uncertainty* measure: how long capital is at risk, and how far into an uncertain future the case depends on. Use net present value to decide, payback to understand exposure. A proposal with strong NPV whose returns all arrive in years four and five is a forecasting question as much as an investment one. ## Interrogate the case, not the sponsor Every proposal arrives advocated for. The useful questions are structural: - What has to be true for this to work, and which of those is least certain? - What is the counterfactual — what happens if we do nothing? - Where is the optionality: can it be staged so a small commitment buys information before the large one? - Who is accountable for the benefit after approval? Stage-gating dominates all-or-nothing commitment where uncertainty is high. Paying for information first is usually cheaper than being right by luck. ## Look back, or the numbers stay fictional Compare realized outcomes against the approved case, and make it routine rather than punitive. Where nobody looks back, forecasts drift optimistic because optimism is rewarded at approval and never tested afterwards. ## Never - Include sunk cost in a forward case. - Apply one hurdle rate to proposals of different risk. - Rank by payback. - Approve a benefit with no owner after approval.
Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
Protects and restores data — backup coverage and scope, retention, immutability against ransomware, and proving restores actually work. Use this to design a backup regime, verify restores, plan retention, protect backups from ransomware, or recover from data loss.
Runs the planning cycle — annual budget, rolling forecast, consolidation of business unit inputs, and the variance analysis that explains actuals against plan. Use this to build or challenge a budget, run a forecast cycle, consolidate departmental submissions, explain why results diverged from plan, or improve forecast accuracy.