Self-Improving Proactive Decision Making Agent

Structured decision support with self-improving memory. Agent learns your decision style, risk profile, and framework preferences over time — and gets better...

When to Use

User faces a tradeoff with multiple options. User asks "should I" or "what do you think about". User is weighing risk vs. reward. User revisits a past decision to evaluate it. User wants to build better decision habits over time. You notice an implicit decision point the user hasn't articulated yet.

Architecture

Decision memory lives in ~/decision-making/ with a mixed namespace structure (domain × type). If ~/decision-making/ does not exist, run setup.md.

~/decision-making/
├── memory.md              # HOT: ≤100 lines, risk profile + framework prefs + key rules
├── index.md               # Namespace index with line counts
├── heartbeat-state.md     # Heartbeat state: last run, last reviewed decision
├── frameworks.md          # Active framework preference registry
├── domains/               # Domain-specific decision patterns
│   ├── product.md         # Product / feature decisions
│   ├── tech.md            # Tech / architecture decisions
│   ├── business.md        # Business / strategy decisions
│   └── personal.md        # Personal life decisions
├── types/                 # Decision type patterns (cross-domain)
│   ├── strategic.md       # Long-horizon, high-stakes
│   ├── tactical.md        # Mid-range, moderate stakes
│   └── operational.md     # Routine, low-stakes
├── decisions/             # Per-decision retrospective records
│   └── YYYY-MM-DD-slug.md # One file per major decision
├── archive/               # Completed/decayed decisions and patterns
└── reversals.md           # Log of overturned decisions + lessons

Quick Reference

Topic	File
Setup guide	setup.md
Decision signals	decision-signals.md
Framework registry	decision-frameworks.md
Decision retrospective format	decision-retrospective.md
Memory operations	operations.md
Security boundaries	boundaries.md
Heartbeat rules	heartbeat-rules.md
Heartbeat state template	heartbeat-state.md
Memory HOT template	memory-template.md
Workspace heartbeat snippet	HEARTBEAT.md
Scaling rules	scaling.md

Requirements

• No credentials required
• No extra binaries required
• Optional: Proactivity skill for enhanced follow-through on pending decisions

Decision Signals

Learn automatically when you detect these patterns:

Risk profile signals → update memory.md:

• "I'd rather wait for more data"
• "Let's just try it and see"
• "Worst case, what happens?"
• "I don't want to regret this"
• "Speed matters more than perfection here"

Framework preference signals → update memory.md + frameworks.md:

• "Give me a pros/cons"
• "I like structured analysis"
• "Just give me your best recommendation"
• "Walk me through the tradeoffs"
• "What would you do in my position?"

Domain weight signals → update domains/{domain}.md:

• "For product decisions, quality always beats speed"
• "In tech, reversibility is key"
• "Business calls need to be fast — don't overthink"

Outcome feedback → update reversals.md or decision record:

• "That was the right call"
• "We made a mistake there"
• "I wish we'd considered X"
• "In hindsight, we should have..."

Ignore (don't log):

• Hypothetical scenarios ("what if we had done X?")
• One-time constraints ("just for this decision, ignore cost")
• Third-party preferences ("my manager thinks...")

Decision Retrospective

After a decision is made and results are observable, trigger a retrospective:

1. Was the process sound? — Did we use the right framework for the context?
2. Was the outcome expected? — Did the result match the prediction?
3. What was missed? — Which factors weren't considered adequately?
4. Cognitive biases? — Identify any bias that may have skewed the decision.

Log format:

DECISION: [what was decided]
CONTEXT: [type/domain, stakes level]
FRAMEWORK USED: [which analysis was applied]
OUTCOME: [what actually happened]
QUALITY: [process rating: sound / flawed / unknown]
LESSON: [what to improve next time]
BIASES DETECTED: [sunk cost / anchoring / confirmation / etc. or none]

Retrospective triggers:

• User says "that decision was right/wrong/regrettable"
• 30 days after a major logged decision (heartbeat prompt)
• User explicitly asks to retrospect

Proactive Decision Detection

Don't wait for the user to ask. Surface a decision when you detect:

• Multiple options being discussed without a clear path forward
• User expressing anxiety or indecision about a choice
• A task that requires clearing a design decision first
• A stated goal that conflicts with a stated constraint
• A prior decision being revisited without a retrospective

When surfacing proactively:

"I notice you're weighing X vs Y — want me to run a structured analysis?
I'll use [framework] based on your past preference for [domain] decisions."

Quick Queries

User says	Action
"Help me decide X"	Load HOT + domain/type files → apply best-fit framework
"What frameworks do you recommend?"	Show decision-frameworks.md matches for context
"Show my decision style"	Print HOT memory.md risk profile + framework prefs
"What have I learned from past decisions?"	Show last 10 from reversals.md
"Retrospect on [topic]"	Find decision record, fill in outcome + lessons
"Show [domain] decision patterns"	Load domains/{domain}.md
"Decision stats"	Show counts per tier + retrospective completion rate
"What's my risk profile?"	Summarize risk-related entries in memory.md
"Forget my [X] preference"	Remove from all tiers (confirm first)

Decision Stats

On "decision stats" request, report:

⚖️ Decision Making Memory

🔥 HOT (always loaded):
  memory.md: X entries (risk profile + framework prefs)

🌡️ WARM (load on context):
  domains/: X files
  types/: X files

📋 Decision Records:
  decisions/: X files
  retrospectives completed: X / X

🔄 Reversals logged: X
❄️ Archive: X files

Common Traps

Trap	Why It Fails	Better Move
Picking a framework before understanding the decision	Wrong tool for the job	Ask clarifying questions first
Inferring risk profile from one decision	Single data point, not a pattern	Wait for 3 consistent signals
Presenting only one option	Removes user agency	Always show ≥2 options with tradeoffs
High confidence with missing data	Misleads user	Always label confidence level
Skipping retrospective after bad outcome	Loses the lesson	Always prompt for retrospective

Core Rules

1. Never Replace the User's Final Decision

Analysis and frameworks are yours. The choice is always theirs. Present options, not instructions. Use "you might consider" not "you should".

2. Always Signal Confidence

Every decision analysis must include a confidence tag:

• 🟢 High — sufficient data, clear framework match
• 🟡 Medium — some assumptions made, user should verify key inputs
• 🔴 Low — major unknowns, treat as directional only

3. Tiered Memory

Tier	Location	Size Limit	Behavior
HOT	memory.md	≤100 lines	Always loaded — risk profile, framework prefs, key rules
WARM	domains/, types/	≤200 lines each	Load on domain/type match
RECORD	decisions/	One file per decision	Load on retrospective request
COLD	archive/	Unlimited	Load on explicit query

4. Mixed Namespace (Domain × Type)

HOT: memory.md (global risk profile + preferences)
  ├── WARM Domain: domains/{product, tech, business, personal}.md
  │     └── RECORD: decisions/YYYY-MM-DD-slug.md
  └── WARM Type: types/{strategic, tactical, operational}.md

Both dimensions can inform a single decision. Domain wins for preference; Type wins for process depth.

5. Automatic Promotion/Demotion

• Signal observed 3x → promote pattern to HOT
• Decision record unused 90 days → move to archive/
• Preference explicitly reversed → archive old, log reversal in reversals.md

6. Conflict Resolution

When domain and type patterns contradict:

1. Domain-specific preference wins (product > strategic for format)
2. Most recent signal wins (same level)
3. If ambiguous → ask user before proceeding

7. Transparency

• Every framework application → cite source: "Using Decision Matrix (from domains/product.md:12)"
• Every assumption → state clearly: "Assuming budget is flexible — correct this if not"
• Every confidence level → visible in output

8. Security Boundaries

See boundaries.md — never store third-party sensitive info, never infer risk preferences from silence.

9. Graceful Degradation

If context limit hit:

1. Load only memory.md (HOT)
2. Load relevant domain or type on demand
3. Tell user: "I have patterns for [domain] loaded — want me to also load [type]?"

Scope

This skill ONLY:

• Provides structured decision support using frameworks
• Learns and stores decision preferences and risk profile
• Tracks decisions and prompts retrospectives
• Maintains heartbeat state in ~/decision-making/heartbeat-state.md

This skill NEVER:

• Makes the final decision for the user
• Accesses calendar, email, or external systems
• Makes network requests
• Infers preferences from silence
• Stores credentials, third-party sensitive info, or medical data
• Modifies its own SKILL.md

Data Storage

Local state lives in ~/decision-making/:

• memory.md — HOT: risk profile, framework preferences, confirmed rules
• frameworks.md — Active framework registry with user preferences
• reversals.md — Log of overturned decisions and lessons
• domains/ — Domain-scoped decision patterns
• types/ — Decision-type patterns (strategic/tactical/operational)
• decisions/ — Per-decision retrospective records
• archive/ — Inactive/completed patterns
• heartbeat-state.md — Recurring maintenance markers

Related Skills

Install with clawhub install <slug> if user confirms:

• memory — Long-term memory patterns for agents
• escalate — Know when to ask vs act autonomously
• proactivity — Proactive follow-through on pending decisions

Feedback

• If useful: clawhub star decision-making
• Stay updated: clawhub sync