systems-thinking

Apply systems thinking: Systems Thinking Pack (boundary, actors/incentives map, feedback loops, second-order effects ledger, leverage points, intervention plan). Use for complex ecosystems, feedback loops, second-order effects, preventing unintended consequences. NOT for trade-off scoring (use evaluating-trade-offs), decision processes (use running-decision-processes), org chart redesign (use organizational-design), or scenario planning (use planning-under-uncertainty). Category: Leadership.

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Install skill "systems-thinking" with this command: npx skills add liqiongyu/lenny_skills_plus/liqiongyu-lenny-skills-plus-systems-thinking

Systems Thinking

Scope

Covers

  • Seeing the "whole system" behind a problem (actors, incentives, feedback loops, culture/rules)
  • Anticipating second- and third-order effects (including time delays)
  • Finding leverage points (small changes with outsized impact)
  • Converting recurring pain into a reusable system (process, automation, or operating mechanism)

When to use

  • "This is a complex ecosystem; we're missing the bigger picture."
  • "What are the second-order effects if we do X?"
  • "We keep solving symptoms—what's the system causing this?"
  • "Map the players + incentives and how they interact."
  • "We need to redesign a process/org without unintended consequences."

When NOT to use

  • The problem is simple/linear and mostly execution (use a project plan/timeline).
  • You need primary user research or data you don't have (do discovery first).
  • You need deep quantitative forecasting/simulation (this skill produces a qualitative map + risks, not a full model).
  • The decision is low-impact and fully reversible (don't over-invest).
  • You need to redesign an org chart, team topology, or reporting lines (use organizational-design; come back here only if you need to map systemic dynamics first).
  • You need to compare discrete options with weighted criteria and pick a winner (use evaluating-trade-offs).
  • You need to plan under uncertainty with scenario trees, hedging strategies, and contingency triggers (use planning-under-uncertainty).
  • You need to run a decision process with roles, meetings, and a decision log (use running-decision-processes).

Inputs

Minimum required

  • The focal decision or problem statement (1–2 sentences)
  • Desired outcome + time horizon (default: 6–12 months)
  • Known constraints/guardrails (trust, safety, compliance, budget, headcount)
  • Known actors/stakeholders (teams, users, partners, regulators, vendors)
  • What has been tried already (and what happened)

Missing-info strategy

  • Ask up to 5 questions from references/INTAKE.md.
  • If answers aren't available, proceed with clearly labeled assumptions and provide 2–3 alternative system framings/boundaries.

Outputs (deliverables)

Produce a Systems Thinking Pack in Markdown (in-chat; or as files if requested) in this order:

  1. Context + System boundary (goal, scope, non-scope, time horizon)
  2. Actors & incentives map (players, goals, constraints, power, conflicts)
  3. System map (key variables + causal links) + feedback loops (reinforcing/balancing) + time delays
  4. Second-/third-order effects ledger for the top 1–3 decisions
  5. Leverage points + intervention plan (actions, owners, sequencing, guardrails)
  6. System-build opportunities (what to automate/standardize to reduce recurring pain)
  7. Risks / Open questions / Next steps (required)

Templates: references/TEMPLATES.md

Workflow (8 steps)

1) Intake + pick the focal decision/problem

  • Inputs: User context; use references/INTAKE.md.
  • Actions: Restate the focal decision/problem, desired outcome, and time horizon; list constraints/guardrails.
  • Outputs: Draft Context + System boundary.
  • Checks: The problem is not a solution in disguise; scope and non-scope are explicit.

2) Define the system boundary (what's "in" vs "out")

  • Inputs: Problem statement + constraints.
  • Actions: Choose a boundary that is useful (not everything). Name the primary outcome metric(s) and a few leading indicators.
  • Outputs: Boundary statement + success measures.
  • Checks: Boundary is tight enough to act on, but wide enough to include key externalities.

3) Map actors + incentives (multi-agent reality)

  • Inputs: Boundary + stakeholder list.
  • Actions: Enumerate actors/players; capture incentives, constraints, power, and likely behaviors.
  • Outputs: Actors & incentives map (table).
  • Checks: Includes at least 1–2 "invisible" actors (e.g., policies, culture norms, platform constraints) if relevant.

4) Build a simple system map (variables + causal links)

  • Inputs: Actors map + known dynamics.
  • Actions: List key variables; map causal links ("A increases B", "C decreases D"); mark time delays.
  • Outputs: System map (text/table) with 10–20 high-signal links.
  • Checks: Links are directional and testable; avoids buzzwords ("alignment", "quality") without definition.

5) Identify feedback loops + time delays

  • Inputs: System map.
  • Actions: Extract reinforcing and balancing loops; note where delays create overshoot/oscillation; flag common traps.
  • Outputs: Feedback loops section (2–6 loops) + delays list.
  • Checks: Each loop has a short "so what" describing the pattern it creates.

6) Run second-/third-order effects on 1–3 candidate moves

  • Inputs: Candidate decisions/actions.
  • Actions: For each move, enumerate first-, second-, and third-order effects; include who wins/loses and what constraints tighten over time.
  • Outputs: Second-/third-order effects ledger.
  • Checks: Includes at least one unintended consequence + one mitigating action per move.

7) Choose leverage points + design interventions (including "build a system")

  • Inputs: Loops + effects ledger.
  • Actions: Identify leverage points (policy, incentives, information flows, tooling, process); propose interventions; include at least one system-build/automation opportunity for recurring pain.
  • Outputs: Leverage points + intervention plan + System-build opportunities.
  • Checks: Each intervention has an owner, a measurable leading indicator, and a guardrail.

8) Quality gate + finalize pack

  • Inputs: All draft sections.
  • Actions: Run references/CHECKLISTS.md and score with references/RUBRIC.md. Add Risks / Open questions / Next steps.
  • Outputs: Final Systems Thinking Pack.
  • Checks: A reader can act without a live meeting; trade-offs and uncertainties are explicit.

Quality gate (required)

Examples

Example 1 (Org/process): "Our on-call load keeps rising and teams are burned out. Map the system and propose leverage points."
Expected: an actors/incentives map (teams, incidents, incentives), feedback loops (firefighting loop), effects ledger for candidate changes, and an intervention plan with guardrails.

Example 2 (Product ecosystem): "We're changing API pricing; what are the second-order effects across partners and customer segments?"
Expected: system boundary + actors map (customers/partners/internal), loops and delays, effects ledger, and a sequencing/mitigation plan.

Boundary example: "Write a status update about this week's tasks." Response: this skill is for complex systems/decisions. Suggest a project update format instead; only use this skill if there's a systemic pattern to diagnose.

Boundary example (neighbor redirect): "Redesign our engineering org to reduce dependencies between teams." Response: this is an org structure redesign, not a systems analysis. Use organizational-design for team topology and reporting lines. Use this skill first only if you need to map the systemic dynamics (feedback loops, incentives) driving the dependency problem before redesigning.

Anti-patterns

  1. Map-everything paralysis — Drawing a system map with 50+ variables and no prioritization, making it impossible to act. Limit the map to 10-20 high-signal links and focus on the loops that explain the observed symptoms.
  2. Feedback loops without "so what" — Identifying reinforcing and balancing loops but not explaining what behavior they produce or how to intervene. Every loop must have a short narrative about the pattern it creates and why it matters.
  3. Confusing correlation with causation — Drawing causal arrows based on co-occurrence rather than testable mechanisms. Each link should state the mechanism ("A increases B because...") not just the direction.
  4. Ignoring time delays — Treating all effects as instantaneous when many systems have 3-12 month lags between action and result. Always mark time delays on the system map and account for them in intervention sequencing.
  5. Leverage-point optimism — Assuming a single intervention will fix the system without considering resistance, adaptation, or second-order effects of the intervention itself. Every intervention needs a guardrail and a "what could go wrong" check.

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systems-thinking | V50.AI