About systems-thinking
A skill for examining the system behind complex problems and decisions involving multiple actors, incentives, and feedback loops. It maps causal links, delays, and leverage points, then turns the analysis into interventions with guardrails.
Key features
- 01Define system boundaries and outcomes
- 02Map actors and their incentives
- 03Trace causal links and feedback loops
- 04Assess delays and second- or third-order effects
- 05Identify leverage points and plan interventions
- 06Find opportunities for automation or standardization
Use cases
- 01Diagnose recurring operational problems
- 02Assess ripple effects of a decision across stakeholders
- 03Analyze side effects of process or organizational changes
- 04Design systemic improvements for persistent problems
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ oldwinter/ skills --skill systems-thinkingAfter installing, type/systems-thinkingin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
skillpack.jsonreferences/CHECKLISTS.mdEXAMPLES.mdINTAKE.mdRUBRIC.mdSOURCE_SUMMARY.mdTEMPLATES.mdWORKFLOW.mdDetails
From the project READMESystems 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).
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:
- Context + System boundary (goal, scope, non-scope, time horizon)
- Actors & incentives map (players, goals, constraints, power, conflicts)
- System map (key variables + causal links) + feedback loops (reinforcing/balancing) + time delays
- Second-/third-order effects ledger for the top 1–3 decisions
- Leverage points + intervention plan (actions, owners, sequencing, guardrails)
- System-build opportunities (what to automate/standardize to reduce recurring pain)
- 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)
- Use references/CHECKLISTS.md and references/RUBRIC.md.
- Always include: Risks, Open questions, Next steps.
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.