fpf-problem-portfolio

This is strategic problem selection, not issue triage. You are managing a portfolio of problems — deciding which problems to invest effort in, balancing exploration vs exploitation, maintaining diversity, and preserving stepping stones.

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Install skill "fpf-problem-portfolio" with this command: npx skills add m0n0x41d/principled-claude-code/m0n0x41d-principled-claude-code-fpf-problem-portfolio

What this skill IS

This is strategic problem selection, not issue triage. You are managing a portfolio of problems — deciding which problems to invest effort in, balancing exploration vs exploitation, maintaining diversity, and preserving stepping stones.

Key questions while filling the template:

  • Which problems are in the zone of proximal development (goldilocks)?

  • Am I over-investing in one type of problem (exploit bias)?

  • Which problems, even if not urgent, could open new solution spaces (stepping stones)?

  • What problem types are NOT in the portfolio but should be?

Prerequisites

  • ≥2 PROB/ANOM-* cards exist (otherwise no portfolio needed)

  • If comparing problems on measurable dimensions: invoke /fpf-characterize for problem-level characterization. Q dimensions in the portfolio table MUST reference CHR-* indicators, not hardcoded defaults.

Output

.fpf/portfolios/PPORT-${CLAUDE_SESSION_ID}--<slug>.md

Constraints (quality bar)

  • C1: Selection rule stated BEFORE applying — not post-hoc rationalized

  • C2: Archive complete — no hidden problems

  • C3: ≥1 active problem has stepping-stone potential (not all exploit)

  • C4: Diversification check honest — flags imbalances (problem type, domain, risk level)

  • C5: Each active problem references existing PROB/ANOM-*

  • C6: Goldilocks filter applied — trivial and impossible problems identified and deferred

  • C7: Q dimensions in NQD table reference CHR-* indicators when characterization exists (not hardcoded impact/feasibility)

Selection policy examples

  • Impact × Feasibility: highest expected value first (exploit-heavy)

  • Learning value: problems that teach the most about the domain (explore-heavy)

  • Barbell: 80% safe + 20% speculative stepping stones

  • Constraint-driven: solve blocking problems first, then expand

Format

Problem Portfolio

  • ID: PPORT-... Scope: ... E/E policy: explore|exploit|barbell

Selection rule

(policy stated before applied, chosen from above or custom)

Active problems (NQD-characterized)

| # | ID | Title | D_c | Q:[impact] | Q:[feasibility] | Q:[stepping-stone] | N | D_p | Pipeline: D_c (goldilocks filter) → Q dominance (Pareto) → N, D_p (tie-breakers). Never collapse Q to single score.

Deferred

| # | ID | Title | Why deferred | Revisit condition |

Diversification check

  • Problem types covered: ...
  • Not covered: ... (flag if imbalanced)
  • Explore/exploit balance: ...

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