Research Finance

research-finance

Financial management of internal R&D programs and portfolios: program budgeting with F&A, burn/runway tracking, and capitalize-vs-expense routing. Every number ships with its assumptions block, and accounting-treatment calls route to a named finance owner — this skill never books an entry.

Purpose

R&D finance partners, program controllers, and operations leads manage money that has already been allocated or raised — not the corporate close, not the next funding round, not finding a grant. This skill structures three recurring decisions:

Three deterministic tools:

1. program_budget_planner.py — Builds a multi-period budget from work-package line items, applies the F&A (indirect) rate to an MTDC-style eligible base, and rolls up direct / F&A / fully-loaded cost per period with an explicit assumptions block.
2. burn_runway_tracker.py — Computes average + trailing burn, runway in periods/months, and whether each value-inflection milestone is reachable before cash runs out. Flags accelerating burn and below-threshold runway.
3. capex_vs_opex_router.py — Scores each R&D cost item against the IAS 38 development-phase criteria (or flags US GAAP ASC 730 expense-as-incurred) and routes it to CAPITALIZE-CANDIDATE / EXPENSE / FINANCE-OWNER-REVIEW with a named owner. Never auto-decides.

When to use

Invoke this skill when:

• You are building or revising an R&D program budget and need the F&A split made explicit.
• A program's runway is in question and you need a milestone-vs-cash read.
• Finance asks whether a development cost can be capitalized and you need a defensible first routing.
• You are preparing a portfolio review and need per-program burn consistency.

Do NOT use this skill to: run corporate DCF / valuation / close (use finance/financial-analysis), discover or position grants (use research/grants), or make the final accounting determination (that is the controller's + auditor's call — this tool only routes).

Workflow

1. Lay out the program — Fill assets/rd_program_budget_template.md with work-package lines, categories, and per-period amounts.
2. Build the budget — Run program_budget_planner.py --input program.json --profile {pharma-rd|biotech|medtech|deep-tech|software-rd|university-lab} --fa-rate <negotiated rate>. Read direct / F&A / fully-loaded rollups + assumptions.
3. Track burn & runway — Run burn_runway_tracker.py --input ledger.json --threshold-months 6. Read runway + milestone verdicts + flags.
4. Route accounting treatment — Run capex_vs_opex_router.py --input costs.json --standard {ifrs|usgaap}. Read the per-item routing; send CAPITALIZE-CANDIDATE and FINANCE-OWNER-REVIEW items to the named owner.
5. Assemble the review — Combine into a program-finance packet. Every number carries its assumptions; treatment calls carry a named owner.

Scripts

All three: stdlib-only, --help, --sample, --output {human,json}.

Onboarding & customization

Run the onboarding questionnaire once before you start — it captures your defaults so every tool in this skill is pre-configured. Customization is the point: the answers actually change tool behavior.

python3 scripts/onboard.py            # interactive (also: --defaults, --set key=value, --reset)
python3 scripts/onboard.py --show     # see the questions + current effective config

Answers are saved to ~/.config/research-ops/research-finance.json (global) or ./.research-ops/research-finance.json (--scope project) and are read automatically by config_loader.py. They set the default R&D-area profile, the default F&A rate, the runway alert threshold, the accounting standard, and the named finance owner printed on capitalize-vs-expense routing. CLI flags always override saved config; RESEARCH_OPS_NO_CONFIG=1 ignores it.

The five questions: R&D area · F&A rate · runway threshold · accounting standard · finance owner.

Optimize with autoresearch (opt-in)

This skill ships an isolated, opt-in bridge to engineering/autoresearch-agent. Only when you ask to "optimize" / "extend runway" / "run a loop" does an autoresearch experiment iteratively improve a program plan against this skill's runway metric. scripts/ar_evaluator.py is the ground-truth evaluator; it prints runway_months: <float> (higher is better).

/ar:setup --domain custom --name extend-runway \
  --target ledger.json \
  --eval "python3 ar_evaluator.py --target ledger.json" \
  --metric runway_months --direction higher
/ar:loop custom/extend-runway

Isolated: no hard dependency — autoresearch runs only on demand, and the loop edits ledger.json, never the evaluator.

References

• references/rd_program_finance_canon.md — IAS 38 (research vs development); ASC 730 + ASC 985-20; Uniform Guidance 2 CFR 200 (F&A); FASB/IFRS capitalization criteria; NICRA basics.
• references/burn_and_portfolio.md — Cooper stage-gate; rNPV / real-options for R&D; risk-adjusted portfolio ROI; burn-rate / runway frameworks; milestone-based budgeting.
• references/indirect_rate_modeling.md — F&A pool composition (facilities + administration); MTDC base; de minimis 10%; fringe/overhead loading; CAS primer.