本文へ移動
cccskills
無料GitHub で公開

research-finance

Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).

インストール方法を見る

含まれるファイル(11)

  • SKILL.md10.4 KB
  • assets/rd_program_budget_template.md1.8 KB
  • references/burn_and_portfolio.md2.7 KB
  • references/indirect_rate_modeling.md2.4 KB
  • references/rd_program_finance_canon.md2.8 KB
  • scripts/ar_evaluator.py2.4 KB
  • scripts/burn_runway_tracker.py6.2 KB
  • scripts/capex_vs_opex_router.py7.5 KB
  • scripts/config_loader.py3.8 KB
  • scripts/onboard.py4.2 KB
  • scripts/program_budget_planner.py7.0 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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

ScriptPurposeProfiles
scripts/program_budget_planner.pyMulti-period budget + F&A split + assumptionspharma-rd, biotech, medtech, deep-tech, software-rd, university-lab
scripts/burn_runway_tracker.pyBurn, runway, milestone-vs-cash alignmentn/a (ledger-driven)
scripts/capex_vs_opex_router.pyIAS 38 / ASC 730 routing to named finance ownerpharma-rd, biotech, medtech, deep-tech, software-rd, university-lab

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.

Assumptions

  • The F&A rate is the most error-prone input. The planner applies whatever rate you pass; it warns you to confirm it is a negotiated NICRA, not a guess.
  • Burn/runway uses the trailing (recent-weighted) burn as the forward run-rate and assumes flat forward spend unless your ledger encodes a ramp.
  • The capex router asserts criteria from your input; asserting "technical feasibility" does not make it true — the named finance owner and auditor validate it.
  • Profiles annotate context (e.g., "most drug R&D is expensed") but do not change the accounting test.

Anti-patterns

  • Stating a budget number without its assumptions. F&A rate, escalation, and base must travel with the number.
  • Auto-deciding capitalize-vs-expense. This tool routes; the controller (and auditor where required) decides.
  • Using lifetime-average burn for runway. Recent burn is the honest forward run-rate; averages hide a slowdown or a ramp.
  • Applying F&A to the full base. Capital equipment, large subaward portions, and certain categories are MTDC-exempt.
  • Confusing this with corporate finance. Valuation, close, and fundraising live in finance/.

Distinct from

Sibling / neighborScopeDifference
finance/financial-analysisCorporate DCF, ratios, close, rolling forecast, SaaS metricsThat is company-level; this is R&D-program-level
research/grantsNIH funding discovery + positioningThat finds funding; this manages money already won
clinical-research (sibling)Study design + feasibility + budget gate-checkThat scopes the study; this funds + tracks the program
ra-qm-teamRegulatory/QM submissionUnrelated — no financial scope

Quick examples

python3 scripts/program_budget_planner.py --sample
python3 scripts/program_budget_planner.py --input program.json --profile university-lab --fa-rate 0.585
python3 scripts/burn_runway_tracker.py --sample --output json
python3 scripts/capex_vs_opex_router.py --sample --standard ifrs

The sample budget excludes the sequencer (capital equipment) and CRO subaward from the F&A base; the capex router routes exploratory screening to EXPENSE, a fully-criteria'd pilot line to CAPITALIZE-CANDIDATE, and a partial-criteria software build to FINANCE-OWNER-REVIEW.

Forcing-question library (Matt Pocock grill discipline)

Walked one at a time by /cs:grill-research-ops or the orchestrator. Recommended answer + canon citation per question. Never bundled.

  1. "Is this spend in the research phase or the development phase — and can you evidence technical feasibility?" Recommended: research = expense; development = capitalize-candidate only with feasibility evidence, routed to a named finance owner. Canon: IAS 38.54-57; ASC 730.

  2. "What F&A / indirect rate are you applying, and is it your negotiated NICRA, a de minimis 10%, or an assumption?" Recommended: use the negotiated rate; if assumed, flag it explicitly. Canon: 2 CFR 200 (Uniform Guidance); NICRA basics.

  3. "What's runway in months at current burn, and does it clear the next value-inflection milestone?" Recommended: runway must cover the milestone plus a buffer; surface the gap. Canon: Cooper stage-gate; SaaS/startup efficiency frameworks (a16z, Bessemer).

  4. "Is portfolio ROI risk-adjusted (rNPV / probability-of-success weighted) or raw NPV?" Recommended: risk-adjusted; raw NPV overstates R&D value. Canon: rNPV drug-development valuation; real-options literature.

  5. "Who is the named finance / controller owner who signs the capitalize-vs-expense treatment?" Recommended: name them — this tool recommends, it never books the entry. Canon: ASC 730 / IAS 38 governance; auditor sign-off requirements.

Walk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke program_budget_planner.py → burn_runway_tracker.py → capex_vs_opex_router.py.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.

日本語の概要は準備中です。原文の説明を表示しています。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.

日本語の概要は準備中です。原文の説明を表示しています。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

Adversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.

日本語の概要は準備中です。原文の説明を表示しています。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

aeo

無料

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

日本語の概要は準備中です。原文の説明を表示しています。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.

日本語の概要は準備中です。原文の説明を表示しています。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

alirezarezvani のスキルをすべて見る

このスキルの問題を報告する