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Philanthropic risk-return assessor

Categorizes grants and funding opportunities along a social-impact risk-return spectrum — from proven/incremental to novel/catalytic — scoring each on 6 risk dimensions and 4 return dimensions, then maps them into a…

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Purpose

Place each grant or funding opportunity on a defined risk-return spectrum using a consistent scoring rubric, then map the full set into a portfolio matrix that reveals whether the foundation is over-weighted toward safe incremental bets or speculative catalytic ones. Gives program officers and investment committees a shared vocabulary and a defensible framework for balancing proven impact against high-upside experimentation.

When to Use

Invoke this skill when the user asks to:

  • Assess the risk profile of a specific grant or funding opportunity
  • Decide between a proven intervention and a riskier, higher-upside one
  • Categorize a slate of proposals along a risk-return spectrum
  • Diagnose whether a grant portfolio is too conservative or too speculative
  • Justify a catalytic or high-risk grant to a skeptical investment committee
  • Build a portfolio allocation strategy across risk tiers

Inputs Required

Before starting, collect:

  • Grant(s) under review: One opportunity or a list — name, grantee, dollar amount, and duration for each
  • Impact goal: The specific outcome the foundation is trying to move (e.g., reduce chronic homelessness in one metro)
  • Intervention description: What the grant funds and the theory of change behind it
  • Evidence base: What's known about whether this approach works (RCTs, pilots, anecdotal, untested)
  • Portfolio context: If assessing balance, the current mix of active grants and their approximate risk levels
  • Foundation risk appetite: Conservative / balanced / catalytic-leaning, if stated
  • Budget available: Total dollars to allocate, if building a portfolio strategy

If portfolio context or risk appetite is missing, assess each grant individually and assume a balanced appetite, noting the assumption.

Instructions

Step 1: Score Risk Across 6 Dimensions

Rate each grant 1 (low risk) to 5 (high risk) on every dimension. Show the score and a one-line justification.

  • Evidence risk: How proven is the intervention? (1 = replicated RCTs; 5 = novel, untested theory of change)
  • Execution risk: Can this grantee actually deliver? (1 = strong track record, mature ops; 5 = new org or unproven capacity)
  • Systems/context risk: How dependent is success on external conditions the foundation can't control? (1 = self-contained; 5 = requires policy change, market shifts, or partner behavior)
  • Measurement risk: Can impact be credibly measured? (1 = clear, attributable metrics; 5 = long time horizons, diffuse or unattributable outcomes)
  • Financial/sustainability risk: What happens when the grant ends? (1 = clear path to sustained funding; 5 = collapses without renewal)
  • Reputational risk: Exposure if it fails publicly. (1 = minimal downside; 5 = high visibility, mission-adjacent, potential for harm)

Sum to a Risk Score (6–30) and classify:

  • 6–13: Low risk / Proven
  • 14–21: Moderate risk / Incremental
  • 22–30: High risk / Novel-Catalytic

Step 2: Score Return Across 4 Dimensions

Rate each grant 1 (modest return) to 5 (transformative return). Return in social-impact terms means depth, breadth, durability, and catalytic potential — not financial yield.

  • Impact depth: How much does it change outcomes for those it reaches? (1 = marginal improvement; 5 = life-altering)
  • Scale potential: How many could ultimately be reached? (1 = fixed small population; 5 = potential to reach a field or system)
  • Durability: How lasting is the change? (1 = benefit ends with funding; 5 = permanent structural or behavioral shift)
  • Catalytic leverage: Could success unlock other funding, policy, or field-wide adoption? (1 = self-contained; 5 = proof point that reshapes the field)

Sum to a Return Score (4–20) and classify: Modest (4–9) / Solid (10–14) / Transformative (15–20).

Step 3: Plot on the Risk-Return Matrix

Place each grant into one of four quadrants:

  • Safe Bets (Low risk, Modest-Solid return): Reliable, defensible, portfolio ballast. The reason the foundation can afford to take risks elsewhere.
  • Efficient Core (Moderate risk, Solid-Transformative return): The sweet spot — proven-enough approaches with meaningful upside. Should be the largest share.
  • Catalytic Bets (High risk, Transformative return): The moonshots — where field-changing impact lives, at the cost of high failure odds. Portfolio-appropriate in limited allocation.
  • Question Marks (High risk, Modest return): Risk without commensurate upside. Flag these for redesign or decline unless there's a strategic reason (e.g., relationship, learning value).

Adapt portfolio theory: like an investment portfolio, the goal is not to minimize risk on every bet but to hold uncorrelated risks so that catalytic failures are absorbed by the stable core while catalytic wins carry the portfolio.

Step 4: Portfolio Balance Analysis (if multiple grants)

Assess the distribution across quadrants:

  • Target allocation ranges (adjust to stated risk appetite): Safe Bets 20–30%, Efficient Core 40–55%, Catalytic Bets 15–30%, Question Marks 0–5%
  • Over-concentration flags: Too much in Safe Bets = under-investing in transformative change; too much in Catalytic = fragile portfolio with high aggregate failure risk; anything in Question Marks needs justification
  • Correlation check: Are the catalytic bets all dependent on the same external condition (e.g., the same policy passing)? If so, the risks are correlated and the portfolio is more fragile than it looks — diversify across independent failure modes

Step 5: Recommendations

For each grant: Fund / Fund with conditions / Redesign / Decline, with a one-sentence rationale tied to its quadrant and the portfolio's needs. For portfolio-level work: name specific rebalancing moves (which risk tier to add to or trim) and one concrete next action.

Output Format

  • Structure: Per-grant scorecards (risk and return dimension tables with justifications), quadrant placement, then portfolio-level balance analysis and ranked recommendations
  • Length: 700–1,100 words for a single grant; 1,200–1,800 for a portfolio
  • Format: Scoring tables for the 6 risk and 4 return dimensions; matrix quadrant assignments as labeled sections; recommendations as a decision list
  • Sections: Risk scoring → Return scoring → Quadrant placement → Portfolio balance analysis (if applicable) → Recommendations
  • Tone: Investment committee memo — analytical, candid about tradeoffs, willing to recommend declining a grant

Quality Checklist

Before delivering output, verify:

  • [ ] Every grant is scored on all 6 risk dimensions and all 4 return dimensions with justifications — no dimension skipped
  • [ ] Risk and Return totals are summed and translated into the correct classification tier
  • [ ] Each grant is placed in exactly one quadrant, consistent with its scores
  • [ ] Portfolio analysis (when applicable) compares actual allocation to target ranges and flags any Question Marks
  • [ ] The correlation check is applied to catalytic bets — not just individual risk in isolation
  • [ ] Every grant gets a clear Fund / Fund with conditions / Redesign / Decline verdict — no hedging

Notes

  • The most common portfolio flaw is over-weighting Safe Bets — foundations rarely go bankrupt, so treating every grant like it must succeed leaves transformative impact on the table
  • Catalytic bets should be sized so that a total loss is survivable and expected; if a high-risk grant's failure would be treated as a scandal, it was mis-sized, not mis-chosen
  • Question Mark grants often survive on relationship inertia — naming them explicitly is one of the highest-value outputs of this assessment
  • When evidence risk is high but the grant is early-stage and small, that's often appropriate learning capital, not a red flag — weight measurement risk toward whether the foundation will actually learn something usable from the outcome

How to use this skill

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  1. Drag this file into a new chat, or copy and paste its contents
  2. Send your task or request as your next message — the AI will apply the skill where it fits

For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills