---

name: strategic-planning description: Use HtmlGraph analytics to make smart work prioritization decisions. Activate when recommending work, finding bottlenecks, assessing risks, or analyzing project impact.

Strategic Planning Skill

When to Activate This Skill

Trigger keywords:

• "what should I work on", "recommend", "prioritize"
• "bottleneck", "blocking", "stuck"
• "risk", "impact", "dependencies"
• "strategic", "roadmap", "plan"

Trigger situations:

• Starting a new session (what to work on?)
• Multiple tasks available (which is most important?)
• Progress seems slow (what's blocking us?)
• Planning major changes (what's the impact?)

---

Core Principle: Data-Driven Decisions

HtmlGraph provides analytics that consider:

• Dependencies - What blocks/enables other work
• Priority - Business importance
• Impact - How many tasks are unlocked
• Risk - Circular deps, complexity
• Parallelism - What can run concurrently

---

Quick Decision Framework

Copyfrom htmlgraph import SDK

sdk = SDK(agent="claude")

# 1. What's blocking progress?
bottlenecks = sdk.find_bottlenecks(top_n=3)
if bottlenecks:
    print("🚧 BOTTLENECKS (fix these first):")
    for bn in bottlenecks:
        print(f"   {bn['id']}: {bn['title']}")
        print(f"      Blocks {bn['blocks_count']} downstream tasks")

# 2. What should I work on?
recs = sdk.recommend_next_work(agent_count=1)
if recs:
    best = recs[0]
    print(f"\n💡 RECOMMENDED: {best['title']}")
    print(f"   Score: {best['score']:.1f}")
    print(f"   Reasons: {', '.join(best['reasons'])}")

# 3. Can we parallelize?
parallel = sdk.get_parallel_work(max_agents=3)
print(f"\n⚡ Parallel capacity: {parallel['max_parallelism']} agents")
print(f"   Ready now: {len(parallel['ready_now'])} tasks")

# 4. Any risks to watch?
risks = sdk.assess_risks()
if risks['high_risk_count'] > 0:
    print(f"\n⚠️ {risks['high_risk_count']} high-risk items")

---

Method Reference

sdk.find_bottlenecks(top_n=5)

Find tasks that block the most downstream work.

Copybottlenecks = sdk.find_bottlenecks(top_n=3)

# Returns list of:
{
    "id": "feat-001",
    "title": "Database Schema",
    "status": "todo",
    "priority": "high",
    "blocks_count": 5,      # How many tasks it blocks
    "blocks": ["feat-002", "feat-003", ...]  # Which tasks
}

Use when:

• Progress feels slow
• Many tasks are "blocked"
• Planning sprint priorities

---

sdk.recommend_next_work(agent_count=1)

Get scored recommendations considering all factors.

Copyrecs = sdk.recommend_next_work(agent_count=3)

# Returns list of:
{
    "id": "feat-001",
    "title": "Authentication",
    "score": 85.5,
    "reasons": [
        "high_priority",
        "unblocks_many",
        "no_dependencies"
    ],
    "priority": "high",
    "status": "todo",
    "blocks_count": 3
}

Scoring factors:

• Priority weight (critical=100, high=75, medium=50, low=25)
• Blocks count (×10 per blocked task)
• No dependencies bonus (+20)
• Bottleneck bonus (+30)

---

sdk.get_parallel_work(max_agents=5)

Find tasks that can run concurrently.

Copyparallel = sdk.get_parallel_work(max_agents=5)

# Returns:
{
    "max_parallelism": 4,          # How many can run at once
    "ready_now": ["f1", "f2", ...], # Level 0 (no deps)
    "blocked": ["f3", "f4", ...],   # Waiting on deps
    "dependency_levels": [          # Topological layers
        ["f1", "f2"],  # Level 0: no deps
        ["f3"],        # Level 1: depends on Level 0
        ["f4", "f5"]   # Level 2: depends on Level 1
    ]
}

Use when:

• Multiple agents available
• Want to speed up delivery
• Planning parallel sprints

---

sdk.assess_risks()

Check for project health issues.

Copyrisks = sdk.assess_risks()

# Returns:
{
    "high_risk_count": 2,
    "circular_dependencies": [],     # Cycles in dep graph
    "single_points_of_failure": [    # Tasks blocking many
        {"id": "feat-001", "blocks": 5}
    ],
    "stale_in_progress": [           # Stuck tasks
        {"id": "feat-002", "days_stale": 7}
    ]
}

Use when:

• Before major releases
• Sprint planning
• Health checks

---

sdk.analyze_impact(feature_id)

Understand what completing a task unlocks.

Copyimpact = sdk.analyze_impact("feat-001")

# Returns:
{
    "unlocks_count": 3,
    "unlocks": ["feat-002", "feat-003", "feat-004"],
    "transitive_impact": 7,  # Total downstream tasks
    "critical_path": True    # On longest dependency chain
}

Use when:

• Deciding between tasks
• Explaining prioritization
• Finding high-leverage work

---

Decision Patterns

Pattern 1: Start of Session

Copysdk = SDK(agent="claude")

# Quick context
info = sdk.get_session_start_info()

print("📊 Project Status:")
print(f"   In-progress: {info['status']['wip_count']}")
print(f"   Bottlenecks: {len(info['bottlenecks'])}")
print(f"   Parallel capacity: {info['parallel']['max_parallelism']}")

# What to work on
if info['recommendations']:
    rec = info['recommendations'][0]
    print(f"\n💡 Start with: {rec['title']}")

---

Pattern 2: Something Is Blocked

Copy# Find what's causing the block
bottlenecks = sdk.find_bottlenecks(top_n=5)

for bn in bottlenecks:
    if bn['status'] == 'todo':
        print(f"🎯 Unblock by completing: {bn['title']}")
        print(f"   This will enable {bn['blocks_count']} tasks")
        break

---

Pattern 3: Planning Parallel Work

Copy# Check if parallelization makes sense
parallel = sdk.get_parallel_work(max_agents=3)
risks = sdk.assess_risks()

if parallel['max_parallelism'] >= 2 and risks['high_risk_count'] == 0:
    print("✅ Safe to parallelize")
    print(f"   Dispatch up to {parallel['max_parallelism']} agents")

    # Get recommendations for each agent
    recs = sdk.recommend_next_work(agent_count=parallel['max_parallelism'])
    for i, rec in enumerate(recs):
        print(f"   Agent {i+1}: {rec['title']}")
else:
    print("⚠️ Sequential execution recommended")
    if risks['high_risk_count'] > 0:
        print(f"   Reason: {risks['high_risk_count']} high-risk items")

---

Pattern 4: Impact Analysis

Copy# Compare two potential tasks
task_a = "feat-001"
task_b = "feat-002"

impact_a = sdk.analyze_impact(task_a)
impact_b = sdk.analyze_impact(task_b)

print(f"Task A unlocks: {impact_a['unlocks_count']} tasks")
print(f"Task B unlocks: {impact_b['unlocks_count']} tasks")

if impact_a['transitive_impact'] > impact_b['transitive_impact']:
    print(f"💡 Prioritize Task A (higher leverage)")
else:
    print(f"💡 Prioritize Task B (higher leverage)")

---

Integration with Smart Plan

The sdk.smart_plan() method combines these analytics:

Copyplan = sdk.smart_plan(
    description="Real-time collaboration",
    create_spike=True,
    timebox_hours=4
)

# Returns context with:
# - bottlenecks_count
# - high_risk_count
# - parallel_capacity
# - Created spike for research

---

Best Practices

DO

1. Check bottlenecks first - High-leverage work
2. Use recommendations - Considers all factors
3. Assess risks before big changes - Avoid surprises
4. Analyze impact - Understand consequences
5. Check parallel capacity - Optimize throughput

DON'T

1. Ignore blocked tasks - They signal bottlenecks
2. Skip risk assessment - Before major releases
3. Parallelize without analysis - May cause conflicts
4. Work on low-impact tasks - When bottlenecks exist

---

Quick Reference

Copyfrom htmlgraph import SDK
sdk = SDK(agent="claude")

# What's blocking us?
sdk.find_bottlenecks(top_n=5)

# What should I do?
sdk.recommend_next_work(agent_count=1)

# Can we parallelize?
sdk.get_parallel_work(max_agents=5)

# Any risks?
sdk.assess_risks()

# What does this unlock?
sdk.analyze_impact("feat-id")

# All-in-one session start
sdk.get_session_start_info()

# Smart planning
sdk.smart_plan("description")