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andurilcode (@andurilcode) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Theory Of Constraints by andurilcode

Name: Theory Of Constraints
Author: andurilcode

Theory Of Constraints

Apply Theory of Constraints (TOC) whenever the user asks why a system, team, pipeline, or process isn't going faster, isn't scaling, keeps hitting the same ceiling, or feels like effort isn't translating into results. Triggers on phrases like "we're stuck", "why is this slow?", "we keep hitting a wall", "throughput is low", "we work hard but don't ship fast", "what's the bottleneck?", "how do we scale this?", or any situation where effort and output feel misaligned. Also trigger when evaluating workflows, delivery pipelines, agent pipelines, or organizational processes — always check for the constraint first before recommending optimizations.

andurilcode Updated Sep 17, 2026

File contents

Theory of Constraints (TOC)

Core principle: Every system has exactly one constraint limiting its throughput at any moment. Improving anything that is not the constraint is waste. Find the constraint, exploit it, then repeat.

The 5 Focusing Steps

Step 1: Identify the Constraint

The single bottleneck — the resource/process/step with least capacity relative to demand.

How to find it:

Where does work pile up? (queue buildup = upstream of constraint)
Where is there idle time? (downstream — starved for input)
What does everyone wait on? (often "that one person" or "that one step")
What's the longest step in the value stream?

Signal: Constraint = where WIP accumulates and delays originate.

Step 2: Exploit the Constraint

Maximize throughput without spending money or major changes first.

Eliminate waste at the constraint (no idle time, no low-value work)
Buffer upstream (never starved)
Offload non-constraint work
Reduce defects feeding in (rework at the constraint is doubly expensive)

Step 3: Subordinate Everything Else

Non-constraint steps serve the constraint, not optimize themselves. A non-constraint at 100% is harmful if it floods the constraint with WIP.

Shift: Stop measuring local efficiency. Measure constraint throughput.

Step 4: Elevate the Constraint

If 2–3 insufficient, invest: capacity, people, tooling — only at the constraint.

Step 5: Repeat

Constraint moves once resolved. Find the new one. Don't let inertia become the constraint.

Output Format

🔍 Constraint Identification

Identified constraint: [Name the specific step, role, resource, or decision]
Evidence: What signals point to this being the constraint?
WIP accumulation point: Where does work pile up?
Downstream starvation: What's blocked waiting for the constraint?

📊 Throughput Analysis

Current flow: [Input → Step A → Step B → ... → Output]
Constraint location in the flow
Estimated throughput loss due to constraint

⚡ Exploit Actions (no-cost first)

What waste can be removed at the constraint immediately?
What low-value work can be offloaded from the constraint?
What buffers should be added upstream?

🔄 Subordination Changes

Which non-constraint steps are currently "optimizing locally" and harming system throughput?
What should they slow down or stop doing?

📈 Elevation Options (if needed)

Investment options to increase constraint capacity
Cost/benefit relative to throughput gain

⚠️ False Bottlenecks to Avoid

Which steps look slow but are actually downstream of the real constraint?
What "improvements" would be wasted effort?

Common Constraint Types

Type	Example	Signal
Capacity constraint	One engineer reviews all PRs	PRs queue up, reviewer is always busy
Knowledge constraint	Only one person knows the system	Everything waits on that person
Decision constraint	Approvals bottleneck execution	Teams idle waiting for sign-off
Handoff constraint	Work crosses team boundaries	Long wait times at team interfaces
Policy constraint	Rules prevent fast action	Work is done but can't ship
Market constraint	Demand is the limit	System has capacity but no demand

Key Mental Shifts

Don't: Optimize every step. Do: Protect and exploit the constraint.
Don't: Measure local efficiency. Do: Measure global throughput.
Don't: Start with investment. Do: Exploit first, then elevate.
Don't: Assume the constraint is fixed. Do: Expect it to move after you fix it.

---name: theory-of-constraintsdescription: Apply Theory of Constraints (TOC) whenever the user asks why a system, team, pipeline, or process isn't going faster, isn't scaling, keeps hitting the same ceiling, or feels like effort isn't translating into results. Triggers on phrases like "we're stuck", "why is this slow?", "we keep hitting a wall", "throughput is low", "we work hard but don't ship fast", "what's the bottleneck?", "how do we scale this?", or any situation where effort and output feel misaligned. Also trigger when evaluating workflows, delivery pipelines, agent pipelines, or organizational processes — always check for the constraint first before recommending optimizations.---# Theory of Constraints (TOC)**Core principle**: Every system has exactly **one constraint** limiting its throughput at any moment. Improving anything that is *not* the constraint is waste. Find the constraint, exploit it, then repeat.---## The 5 Focusing Steps### Step 1: Identify the ConstraintThe single bottleneck — the resource/process/step with least capacity relative to demand.**How to find it:**- Where does work **pile up**? (queue buildup = upstream of constraint)- Where is there **idle time**? (downstream — starved for input)- What does everyone **wait on**? (often "that one person" or "that one step")- What's the **longest step** in the value stream?Signal: *Constraint = where WIP accumulates and delays originate.*### Step 2: Exploit the ConstraintMaximize throughput **without spending money or major changes first.**- Eliminate waste at the constraint (no idle time, no low-value work)- Buffer upstream (never starved)- Offload non-constraint work- Reduce defects feeding in (rework at the constraint is doubly expensive)### Step 3: Subordinate Everything ElseNon-constraint steps **serve the constraint**, not optimize themselves. A non-constraint at 100% is harmful if it floods the constraint with WIP.**Shift**: Stop measuring local efficiency. Measure **constraint throughput**.### Step 4: Elevate the ConstraintIf 2–3 insufficient, invest: capacity, people, tooling — only at the constraint.### Step 5: RepeatConstraint moves once resolved. Find the new one. Don't let inertia become the constraint.---## Output Format### 🔍 Constraint Identification- **Identified constraint**: [Name the specific step, role, resource, or decision]- **Evidence**: What signals point to this being the constraint?- **WIP accumulation point**: Where does work pile up?- **Downstream starvation**: What's blocked waiting for the constraint?### 📊 Throughput Analysis- Current flow: [Input → Step A → Step B → ... → Output]- Constraint location in the flow- Estimated throughput loss due to constraint### ⚡ Exploit Actions (no-cost first)- What waste can be removed at the constraint immediately?- What low-value work can be offloaded from the constraint?- What buffers should be added upstream?### 🔄 Subordination Changes- Which non-constraint steps are currently "optimizing locally" and harming system throughput?- What should they slow down or stop doing?### 📈 Elevation Options (if needed)- Investment options to increase constraint capacity- Cost/benefit relative to throughput gain### ⚠️ False Bottlenecks to Avoid- Which steps *look* slow but are actually downstream of the real constraint?- What "improvements" would be wasted effort?---## Common Constraint Types| Type | Example | Signal ||------|---------|--------|| **Capacity constraint** | One engineer reviews all PRs | PRs queue up, reviewer is always busy || **Knowledge constraint** | Only one person knows the system | Everything waits on that person || **Decision constraint** | Approvals bottleneck execution | Teams idle waiting for sign-off || **Handoff constraint** | Work crosses team boundaries | Long wait times at team interfaces || **Policy constraint** | Rules prevent fast action | Work is done but can't ship || **Market constraint** | Demand is the limit | System has capacity but no demand |---## Key Mental Shifts- **Don't**: Optimize every step. **Do**: Protect and exploit the constraint.- **Don't**: Measure local efficiency. **Do**: Measure global throughput.- **Don't**: Start with investment. **Do**: Exploit first, then elevate.- **Don't**: Assume the constraint is fixed. **Do**: Expect it to move after you fix it.

Frequently asked questions