From rune
Performs step-by-step analysis of multi-variable decisions: classifies reversibility, maps dependencies, detects biases, tracks second-order effects. For interdependent factors in architecture, debugging, planning.
How this skill is triggered — by the user, by Claude, or both
Slash command
/rune:sequential-thinkingThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Multi-variable analysis utility for decisions where factors are interdependent and order of reasoning matters. Receives a decision problem, classifies reversibility, detects cognitive biases, maps variable dependencies, processes them in dependency order, checks for second-order effects, and returns a structured decision tree with final recommendation. Stateless — no memory between calls.
Multi-variable analysis utility for decisions where factors are interdependent and order of reasoning matters. Receives a decision problem, classifies reversibility, detects cognitive biases, maps variable dependencies, processes them in dependency order, checks for second-order effects, and returns a structured decision tree with final recommendation. Stateless — no memory between calls.
None — pure L3 reasoning utility.
debug (L2): multi-factor bugs with interacting causesplan (L2): complex architecture with many trade-offsbrainstorm (L2): evaluating approaches with many variablesInvoke this skill when:
Do NOT use for simple linear analysis — problem-solver is more efficient for single-dimension reasoning.
decision: string — the decision or problem to analyze
variables: string[] — (optional) pre-identified factors; if omitted, skill identifies them
constraints: string[] — (optional) hard limits that eliminate options
goal: string — (optional) success criteria or desired outcome
Before investing analytical effort, classify the decision:
| Type | Definition | Analytical Effort |
|---|---|---|
| Two-way door | Reversible, can iterate, low switching cost | Decide quickly, set review date. Light analysis. |
| One-way door | Irreversible, high stakes, costly to reverse | Full sequential analysis. Deep reasoning. |
| Partially reversible | Some aspects reversible, some not | Full analysis on irreversible aspects, light on reversible. |
If two-way door → streamline: skip Step 4 (second-order) and Step 5 (bias cross-check). State reasoning.
List every factor that affects the decision. For each variable, record:
If the caller provided variables, validate and expand the list. If omitted, derive from the decision statement.
For each pair of variables, determine if a dependency exists:
[A] constrains [B]: choosing a value for A limits valid values for B[A] influences [B]: A affects the cost/benefit calculation for B but does not eliminate options[A] independent of [B]: no relationshipDocument dependencies as: [Variable A] → [Variable B]: [type and reason]
Identify which variables have the most outbound dependencies — those must be resolved first.
Sort variables from most-constrained (fixed / most depended upon) to least-constrained (free / most flexible). Process in that order:
For each variable in sequence:
Do not jump ahead — each step must reference the conclusions of prior steps.
Running state block at each step:
State after Step N:
- [Variable A]: resolved to [value] because [reason]
- [Variable B]: resolved to [value] because [reason]
- Remaining: [Variable C], [Variable D]
After all variables are resolved, apply second-order thinking:
For each resolved variable, ask: "And then what?"
| Variable | First-Order Effect | Second-Order Effect | Risk Level |
|---|---|---|---|
| [A = value] | [immediate consequence] | [consequence of consequence] | low/medium/high |
Flag any second-order effect that:
If a dangerous second-order effect is found → revisit the affected variable with this new information.
Check the analysis for the 3 biases most dangerous to multi-variable decisions:
| Bias | Detection Question | If Detected |
|---|---|---|
| Anchoring | Did the first variable we resolved disproportionately constrain all others? Would the result differ if we started from a different variable? | Re-evaluate with a different starting variable. Compare results. |
| Status Quo | Did we give an unfair advantage to "keep current approach" for any variable? Would we choose this if starting from scratch? | Evaluate current state with same rigor as alternatives. |
| Overconfidence | How confident are we in each variable's resolution? Are confidence intervals wide enough? | Assign explicit confidence % to each resolution. Flag any > 90% without strong evidence. |
If bias is detected → note it in the report and state whether it changes the recommendation.
After all variables are resolved and cross-checked:
high (all variables resolved cleanly), medium (1-2 ambiguous), low (major uncertainty remains)Return the full decision tree and recommendation in the output format below.
## Sequential Analysis: [Decision]
### Reversibility: [two-way door / one-way door / partially reversible]
[One sentence reasoning. If two-way: "Light analysis — decide quickly, review in [timeframe]."]
### Variables Identified
| Variable | Possible Values | Type |
|----------|----------------|------|
| [A] | [options] | controllable / fixed |
| [B] | [options] | controllable / fixed |
### Dependency Map
- [A] → [B]: [type] — [reason]
- [C] → [A]: [type] — [reason]
### Step-by-Step Evaluation
1. **[Variable A]** (no dependencies — evaluate first)
- Options: [x, y, z]
- Reasoning: [why one is better given constraints]
- Conclusion: **[chosen value]** (confidence: X%)
- State: { A: [value] }
2. **[Variable B]** (depends on A = [value])
- Options remaining: [filtered list]
- Reasoning: [updated analysis given A's value]
- Conclusion: **[chosen value]** (confidence: X%)
- State: { A: [value], B: [value] }
...
### Second-Order Effects (one-way door only)
| Variable | First-Order | Second-Order | Risk |
|----------|------------|-------------|------|
| [A] | [effect] | [and then what?] | low/medium/high |
### Bias Check
- ⚠️ [Bias]: [detection result] → [action taken or "not detected"]
### Ambiguities
- [variable or factor that could not be fully resolved, and what information would resolve it]
### Final Recommendation
[synthesized conclusion incorporating all resolved variables, with confidence level]
- **Confidence**: high | medium | low
- **Key assumption**: [the most critical assumption this recommendation depends on]
- **Review date**: [when to revisit this decision, especially for two-way doors]
| Failure Mode | Severity | Mitigation |
|---|---|---|
| Evaluating variable B before all variables constraining B are resolved | CRITICAL | Dependency order is mandatory — sort by constraint depth first |
| Dependency cycle detected but not flagged | HIGH | Break cycle by treating one variable as a fixed assumption — flag explicitly |
| More than 8 variables without grouping | MEDIUM | Group related variables — keep tractable, not exhaustive |
| Final recommendation missing confidence level | MEDIUM | Confidence (high/medium/low) is required — ambiguities drive confidence down |
| Full analysis on a two-way door decision | MEDIUM | Step 0 classifies reversibility — two-way doors get light analysis |
| Ignoring second-order effects on irreversible decisions | HIGH | Step 4 is mandatory for one-way doors — "and then what?" |
| Anchoring on first variable resolved | MEDIUM | Bias cross-check Step 5 — test if different starting variable changes result |
| No review date on reversible decisions | LOW | Two-way doors MUST include a review date — iterate, don't commit |
~500-1500 tokens input, ~500-1200 tokens output. Sonnet for reasoning depth.
npx claudepluginhub rune-kit/rune --plugin @rune/analyticsCategorises a decision by reversibility (one-way vs two-way door) and applies the appropriate level of process rigour. Useful for avoiding analysis paralysis on reversible choices and recklessness on irreversible ones.
Applies structured reasoning to complex coding problems using 19 analytical frameworks, 12 bias detectors, 10 decomposition methods, 10 mental models, Cynefin classification, ethical checks, and communication patterns.
Applies canonical 7D meta-cognitive reasoning to complex problems with dependencies and tradeoffs, delivering clear answers with confidence scores and caveats.