reflectionLearns when to stop and review. Self-critiques before showing you, fewer revision rounds.
Install via ClawdBot CLI:
clawdbot install ivangdavila/reflectionAgents repeat mistakes. Not because they're incapable β because they forget. This skill changes that. Your agent pauses before delivering, catches its own blind spots, and remembers lessons for next time.
User needs quality assurance beyond "looks good to me." Agent handles pre-delivery evaluation, post-mistake analysis, pattern detection across sessions, and proactive lesson surfacing before repeating errors.
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β SELF REFLECTION LOOP β
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β
ββββββββββββββββββββββΌβββββββββββββββββββββ
βΌ βΌ βΌ
βββββββββββ ββββββββββββ βββββββββββ
β PRE β β POST β βPATTERN β
βDELIVERY β β MISTAKE β βDETECTED β
ββββββ¬βββββ ββββββ¬ββββββ ββββββ¬βββββ
β β β
β "Before I send β "User corrected β Same mistake
β this, let me β me. Why?" β 3 times...
β double-check" β β
β β β
βββββββββββββββββββββ΄βββββββββββββββββββββ
β
βΌ
βββββββββββββββββββ
β 7-DIMENSION β
β EVALUATION β
β (30 seconds) β
ββββββββββ¬βββββββββ
β
βββββββββββββββββ΄ββββββββββββββββ
βΌ βΌ
βββββββββββββββ βββββββββββββββ
β ALL CLEAR β β ISSUE FOUND β
β Deliver β β Fix first β
βββββββββββββββ ββββββββ¬βββββββ
β
βΌ
βββββββββββββββββββ
β LOG LESSON β
β Miss β Root β
β β Prevention β
ββββββββββ¬βββββββββ
β
βΌ
βββββββββββββββββββ
β INJECT NEXT β
β TIME β
β "Before we β
β proceed..." β
βββββββββββββββββββ
Before sending important work, pause. 30 seconds. Quick scan of 7 dimensions.
When: Code, architecture, strategy, any deliverable the user will act on.
User corrected you. That's data. Capture it before the session ends.
When: User says "actually...", "no, that's wrong", "I meant...", frustration signals.
Same category appearing 3+ times? That's not coincidence β it's a blind spot.
When: After logging 5 reflections, weekly review, or heartbeat trigger.
Memory lives in ~/reflection/. See memory-template.md for setup.
~/reflection/
βββ memory.md # Status + preferences + stats
βββ reflections.md # Log (most recent first)
βββ patterns.md # Detected patterns
βββ archive/ # Monthly archives
| Topic | File |
|-------|------|
| Setup process | setup.md |
| Memory template | memory-template.md |
| Evaluation dimensions | dimensions.md |
| Reflection prompts | prompts.md |
Before significant work, scan ~/reflection/patterns.md. Surface relevant lessons:
"Before we proceed β I have a lesson from past work on [topic]: [summary]."
| # | Dimension | Question |
|---|-----------|----------|
| 1 | Correctness | Does it solve the stated problem? |
| 2 | Completeness | Edge cases covered? Assumptions stated? |
| 3 | Clarity | Immediately understandable? |
| 4 | Robustness | What could break this? |
| 5 | Efficiency | Unnecessary complexity? |
| 6 | Alignment | What user actually wants? |
| 7 | Pride | Would I sign my name on this? |
If any dimension scores below 7/10 β fix before delivering.
When user corrects you:
~/reflection/reflections.md:## YYYY-MM-DD | [category]
**Miss:** What went wrong
**Root:** Why (5 whys)
**Fix:** Prevention rule
Default: technical, communication, assumptions, process, scope
Move processed reflections to ~/reflection/archive/YYYY-MM.md. Keep reflections.md lean.
Days since repeated mistake. Resets on pattern recurrence. Celebrate milestones.
ββββββββββββββββ ββββββββββββββββ ββββββββββββββββ ββββββββββββββββ
β EMERGING β βββΆ β ACTIVE β βββΆ β MONITORING β βββΆ β RESOLVED β
β 2 similar β β 3+ times β β Prevention β β 30 days β
β reflections β β β create β β in place β β clean β
ββββββββββββββββ β rule β ββββββββββββββββ ββββββββββββββββ
ββββββββββββββββ
Patterns in ~/reflection/patterns.md:
## [Pattern Name]
category: technical
frequency: 4 occurrences
status: active | monitoring | resolved
**Pattern:** What keeps happening
**Root:** Why this pattern exists
**Prevention:** Rule to break it
**Last seen:** YYYY-MM-DD
**Streak:** X days without recurrence
The skill's real value: surfacing lessons BEFORE you repeat mistakes.
How it works:
~/reflection/patterns.md for active patternsExample:
"Before we build this API β I have a lesson about timeout handling from a previous project. Let me make sure to include proper error timeouts this time."
On first use, read setup.md for integration guidelines. Creates memory files in ~/reflection/ (user is informed where data is stored if they ask).
| Trap | Consequence |
|------|-------------|
| Reflecting without logging | Lesson lost with session |
| Vague root causes | "Made mistake" doesn't prevent recurrence |
| No prevention rule | Same mistake WILL happen again |
| Ignoring patterns | Individual mistakes are noise; patterns are signal |
| Over-reflecting | 30 seconds pre-delivery, not 5 minutes |
Install with clawhub install if user confirms:
memory β persistent memory patternsdecide β decision-making autonomylearning β adaptive learning systemclawhub star reflectionclawhub syncGenerated Mar 1, 2026
Before deploying code, the agent pauses to evaluate its correctness, robustness, and clarity using the 7-dimension framework. This catches bugs, edge cases, and inefficiencies early, reducing post-deployment fixes and improving code quality in agile or DevOps environments.
When crafting responses to customer inquiries, the agent self-reflects to ensure alignment with user needs and clarity. It logs corrections from user feedback to detect patterns like miscommunication or assumptions, enhancing support accuracy and reducing repeat issues in service industries.
Before delivering financial insights or reports, the agent reviews for completeness and correctness, checking assumptions and edge cases. Post-mistake analysis helps identify recurring errors in data interpretation, improving reliability for accounting or investment firms.
When generating learning materials or answers, the agent uses pre-delivery evaluation to ensure clarity and alignment with educational goals. Pattern detection flags repeated misunderstandings, allowing proactive lesson injection to adapt content for better student outcomes in e-learning platforms.
Before finalizing campaign plans, the agent reflects on efficiency and alignment with target audience needs. It logs user corrections to refine messaging and detect patterns in audience response, optimizing strategies for advertising or digital marketing agencies.
Offer the skill as a premium add-on for AI agent platforms, charging monthly fees based on usage tiers. Revenue comes from subscriptions that include advanced features like pattern detection analytics and priority support, targeting enterprises needing quality assurance.
Provide custom integration and training services to help businesses implement the skill into their existing AI workflows. Revenue is generated through project-based fees and ongoing maintenance contracts, ideal for organizations with complex agent deployments.
Offer a basic version of the skill for free to attract users, with premium features like detailed pattern reports and automated lesson injection available for a fee. Revenue streams include upgrades and in-app purchases for enhanced functionality.
π¬ Integration Tip
Set up the memory directory and configure triggers based on your workflow to ensure seamless pre-delivery checks and post-mistake logging.
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