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Get Started Free →Execute evidence-based decision-making through 6-stage causal flow. Use for known processes, operational execution, and decisions with clear cause-effect chains.
.claude/skills/aiskillstore-reasoning-causal/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 8% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 173% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 36% | 0% |
Execute systematic cause-effect reasoning. The logic of process and action.
Threads are the execution layer for goals. Goals define what to achieve; threads define how.
Goal (goal-setter)
└── Subgoal
└── Thread (reasoning-causal) ← executes via 6-stage flow
└── Learning → updates Goal state (goal-tracker)Thread types:
goal_id in metadataCausal : Input → Hypothesis → Implication → Decision → Action → Learning
Where:
Input : Observation × Context → FactualStatement
Hypothesis : FactualStatement × CanvasAssumption → TestableHypothesis
Implication : TestableHypothesis → (Impact × Probability × Timeline)
Decision : Implication × Alternatives → Commitment
Action : Commitment → [ExecutableTask]
Learning : [ExecutedTask] × Outcomes → CanvasUpdate × GoalUpdate| Type | Location | Use For | |------|----------|---------| | Business | threads/operations/{name}/ | Strategic decisions, product changes | | Sales | threads/sales/{name}/ | Deal pipelines, prospects | | Marketing | threads/marketing/{name}/ | Campaigns, content launches | | Engineering | threads/engineering/{name}/ | Requirements → specifications |
Thread-specific details: See references/threads/{type}.md
Execute stages sequentially. Each stage produces a markdown file in the thread directory.
File: 1-input.md Purpose: Capture factual observation that triggers the flow.
Content:
Rules:
Detail: references/stages/input.md
File: 2-hypothesis.md Purpose: Link observation to Canvas assumption being tested.
Content:
Rules:
strategy/canvas/10.assumptions.mdDetail: references/stages/hypothesis.md
File: 3-implication.md Purpose: Analyze business impact with numbers.
Content:
Rules:
Detail: references/stages/implication.md
File: 4-decision.md Purpose: Make official commitment with impact score.
Content:
Impact Scoring:
| Score | Action | |-------|--------| | < 0.8 | Auto-execute | | ≥ 0.8 | Flag for human approval |
Mode-Aware Formulas:
VENTURE: Impact = (Strategic Value × Market Size × Defensibility) / 3 BOOTSTRAP: Impact = (Revenue Impact × Time to Cash × Margin) / 3
Check strategy/canvas/00-business-model-mode.md for mode.
Detail: references/stages/decision.md
File: 5-actions.md or 5-actions/ directory Purpose: Generate executable tasks.
Content:
Action Types by Thread:
| Thread | Action Types | Skills | |--------|--------------|--------| | Sales | lead-intake, qualify, demo, pilot, close | sales-* | | Marketing | research, create, publish, promote, measure | marketing-* | | Engineering | requirements, specification, implementation | engineering-* | | Business | varies by decision | - |
Detail: references/stages/actions.md
File: 6-learning.md Purpose: Document outcomes and update Canvas + Goal.
Content:
Rules:
strategy/canvas/10.assumptions.mdGoal Integration:
If thread.goal_id exists:
1. Read goal from strategy/goals/active/{goal_id}.md
2. Update subgoal status (pending → completed)
3. Extract metrics from learning for goal state
4. Check if goal success criteria met
5. If all subgoals complete → mark goal completedDetail: references/stages/learning.md
1. Receive subgoal from goal-setter
2. Create thread: threads/{type}/{name}/
3. Set meta.json with goal_id and subgoal
4. Execute stages 1-6 sequentially
5. At Stage 4: Calculate impact, flag if ≥0.8
6. At Stage 6: Update Canvas AND goal state
7. Notify goal-tracker of completion1. Receive signal (feedback, anomaly, opportunity)
2. Create thread: threads/{type}/{name}/
3. Set meta.json without goal_id
4. Execute stages 1-6 sequentially
5. At Stage 4: Calculate impact, flag if ≥0.8
6. At Stage 6: Update Canvas
7. Optionally: Link to existing goal or spawn new goalthreads/{type}/{name}/
├── meta.json # Thread metadata (includes goal linkage)
├── 1-input.md # Factual observation
├── 2-hypothesis.md # Canvas assumption link
├── 3-implication.md # Impact analysis
├── 4-decision.md # Commitment + impact score
├── 5-actions.md # Executable tasks
└── 6-learning.md # Outcomes + Canvas/Goal updatejson{ "id": "thread-{type}-{name}", "type": "business | sales | marketing | engineering", "status": "active | completed | blocked", "created": "YYYY-MM-DD", "updated": "YYYY-MM-DD", "goal_id": "g-{goal-id}", // Optional: linked goal "subgoal": "SG1", // Optional: which subgoal "stage": 1-6, "impact_score": 0.0-1.0 }
Goal-linked threads:
goal_id references strategy/goals/active/{goal-id}.mdsubgoal indicates which subgoal this thread executesReactive threads (no goal):
goal_id is null or absentAI Autonomous (Impact <0.8):
Human Review (Impact ≥0.8):
references/
├── stages/ # Stage execution details
│ ├── input.md
│ ├── hypothesis.md
│ ├── implication.md
│ ├── decision.md
│ ├── actions.md
│ └── learning.md
└── threads/ # Thread type specifics
├── operations.md
├── sales.md
├── marketing.md
└── engineering.mdNote: Action execution uses flat skills (sales-*, marketing-*, engineering-*) not templates.
Every decision flows through 6 stages. No shortcuts.
Goals are primary. Threads execute goals. Reactive threads are fallback.
This skill:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 33,282 | 28,324 | -15% | 1 | 1 | 0% | 6,099 | 6,582 | +8% | 0 | 0 | — |
case-02 | fail→pass | 35,828 | 27,350 | -24% | 1 | 1 | 0% | 4,810 | 6,555 | +36% | 0 | 0 | — |
case-03 | fail→pass | 39,310 | 23,359 | -41% | 1 | 1 | 0% | 7,937 | 6,772 | -15% | 0 | 0 | — |
case-04 | fail→fail | 29,909 | 19,130 | -36% | 1 | 1 | 0% | 153 | 4,942 | +3130% | 0 | 0 | — |
case-09 | fail→pass | 12,911 | 12,964 | +0% | 1 | 1 | 0% | 1,378 | 3,756 | +173% | 0 | 0 | — |
case-05 | fail→fail | 19,311 | 27,544 | +43% | 1 | 1 | 0% | 3,499 | 5,622 | +61% | 0 | 0 | — |
case-06 | fail→fail | 11,273 | 38,511 | +242% | 1 | 1 | 0% | 1,112 | 5,975 | +437% | 0 | 0 | — |
case-07 | fail→pass | 16,763 | 13,229 | -21% | 1 | 1 | 0% | 2,366 | 3,216 | +36% | 0 | 0 | — |
case-08 | fail→pass | 22,926 | 20,458 | -11% | 1 | 1 | 0% | 1,502 | 2,766 | +84% | 0 | 0 | — |
case-10 | fail→pass | 18,310 | 10,330 | -44% | 1 | 1 | 0% | 2,441 | 3,187 | +31% | 0 | 0 | — |
case-11 | fail→pass | 19,468 | 7,799 | -60% | 1 | 1 | 0% | 1,829 | 2,705 | +48% | 0 | 0 | — |
case-12 | pass→pass | 13,764 | 23,085 | +68% | 1 | 1 | 0% | 1,363 | 3,130 | +130% | 0 | 0 | — |
case-13 | fail→pass | 15,635 | 22,764 | +46% | 1 | 1 | 0% | 1,820 | 3,172 | +74% | 0 | 0 | — |
case-14 | fail→pass | 21,734 | 10,570 | -51% | 1 | 1 | 0% | 2,027 | 2,607 | +29% | 0 | 0 | — |
case-15 | fail→pass | 16,719 | 28,036 | +68% | 1 | 1 | 0% | 1,896 | 2,609 | +38% | 0 | 0 | — |
case-16 | fail→pass | 27,672 | 7,538 | -73% | 1 | 1 | 0% | 1,685 | 2,639 | +57% | 0 | 0 | — |
case-17 | fail→pass | 15,517 | 9,226 | -41% | 1 | 1 | 0% | 1,681 | 2,743 | +63% | 0 | 0 | — |
case-18 | fail→pass | 20,151 | 24,476 | +21% | 1 | 1 | 0% | 1,783 | 2,740 | +54% | 0 | 0 | — |
case-19 | fail→pass | 13,280 | 23,149 | +74% | 1 | 1 | 0% | 1,318 | 3,028 | +130% | 0 | 0 | — |
case-20 | fail→pass | 17,680 | 31,472 | +78% | 1 | 1 | 0% | 1,600 | 2,918 | +82% | 0 | 0 | — |
case-21 | fail→pass | 44,619 | 21,634 | -52% | 1 | 1 | 0% | 888 | 2,712 | +205% | 0 | 0 | — |
case-22 | fail→pass | 49,406 | 6,292 | -87% | 1 | 1 | 0% | 530 | 2,481 | +368% | 0 | 0 | — |
case-23 | fail→pass | 34,820 | 23,311 | -33% | 1 | 1 | 0% | 2,325 | 3,174 | +37% | 0 | 0 | — |
case-24 | pass→pass | 14,282 | 33,060 | +131% | 1 | 1 | 0% | 1,035 | 2,646 | +156% | 0 | 0 | — |
case-25 | pass→pass | 41,274 | 22,232 | -46% | 1 | 1 | 0% | 1,594 | 2,889 | +81% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 25 cases were attempted, and 24 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +76 percentage points is the difference between those two pass rates over the 24 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.