Claudeception
Continuous learning system that extracts reusable knowledge from work sessions into Claude Code skills. Each extracted skill makes future sessions smarter.
When to Extract
Extract when you encounter:
- Non-obvious solutions — Required >10 min investigation, not in docs
- Error resolution — Misleading error messages, non-obvious root causes
- Workaround discovery — Tool/framework limitations requiring experimentation
- Configuration insights — Project-specific setups differing from standard
- Trial-and-error success — Multiple approaches before finding what worked
- Self-critique catches — A predicted risk actually materialized (high-value signal)
- Path efficiency insights — A systemic execution pattern worth improving
Dual-Track Classification
Before creating, classify the knowledge:
| Track | When | Template sections |
|---|---|---|
| Bug fix | Defect, failure, error resolution | Problem, Symptoms, Root Cause, Solution, Prevention |
| Knowledge | Best practice, pattern, workflow optimization | Context, Guidance, Why This Matters, When to Apply |
This determines the skill's section structure.
Extraction Process
Step 1: Overlap Detection (BEFORE creating)
Search existing skills for overlap across 5 dimensions:
| Dimension | What to compare |
|---|---|
| Problem statement | Same underlying issue? |
| Root cause | Same technical cause? |
| Solution approach | Same fix? |
| Referenced files | Same code paths? |
| Prevention rules | Same advice? |
Scoring: Count matching dimensions.
| Overlap | Action |
|---|---|
| High (4-5 match) | Update existing skill with fresher context |
| Moderate (2-3 match) | Create new, add See also: cross-reference |
| Low (0-1 match) | Create new normally |
Why: Two skills describing the same problem will drift apart. Update rather than duplicate.
Step 2: Research (When Appropriate)
Search the web when the topic involves specific technologies, frameworks, or APIs. Skip for project-specific internal patterns. Cite sources in a References section.
Step 3: Structure the Skill
Use this template — adapt sections based on track:
---
name: [descriptive-kebab-case-name]
description: |
[Trigger conditions ONLY. Start with "Use when...". Include specific symptoms,
error messages, contexts. NEVER summarize the skill's workflow here.]
---
# [Skill Name]
## Problem / Context
[Bug track: What broke. Knowledge track: What situation prompted this.]
## Symptoms / When to Apply
[Bug track: Observable symptoms. Knowledge track: Conditions where this applies.]
## Solution / Guidance
[Step-by-step fix or recommended practice with code examples.]
## Prevention / Why This Matters
[Bug track: How to avoid recurrence. Knowledge track: Impact of following/not following.]
## Notes
[Caveats, edge cases, See also: links to related skills]
## References
[Optional: URLs to official docs or resources]
Step 4: Write Effective Descriptions (CSO)
Description = Triggering conditions ONLY. Never summarize the workflow.
Why: Testing revealed that descriptions summarizing workflow cause agents to follow the description instead of reading the full skill. A description saying "code review between tasks" caused agents to do ONE review, when the skill required two.
# BAD: Summarizes workflow — agent shortcuts to this
description: Use when executing plans — dispatches subagent per task with code review
# GOOD: Trigger conditions only — agent reads the full skill
description: Use when executing implementation plans with independent tasks
Format rules:
- Start with "Use when..."
- Include specific symptoms, error messages, contexts
- Keep under 500 characters
- Third person (injected into system prompt)
- Include technology names if skill is technology-specific
Step 5: Discoverability Check
After creating a skill, verify:
- The description contains keywords someone would search for
- The name is descriptive (verb-first preferred:
fixing-X, notX-fix) - If the project has a CLAUDE.md, consider whether it should reference this skill category
Step 6: Save
- Project-specific:
.claude/skills/[skill-name]/SKILL.md - User-wide:
~/.claude/skills/[skill-name]/SKILL.md - Heavy reference (>100 lines) goes in
references/subdirectory - Reusable scripts go in
scripts/subdirectory
Quality Gates
Before finalizing:
- Description is trigger-only, no workflow summary (CSO)
- Solution verified to work (not theoretical)
- Specific enough to be actionable
- General enough to be reusable
- No sensitive information
- Overlap check completed (Step 1)
- No duplicate of existing skill or official docs
Anti-Patterns
| Anti-pattern | Why it's bad |
|---|---|
| Over-extraction | Mundane solutions don't need skills |
| Vague description | "Helps with React" won't surface when needed |
| Workflow in description | Agent shortcuts, skips reading full skill |
| Unverified solution | Only extract what actually worked |
| Duplication | Two skills on same topic drift apart over time |
| Narrative storytelling | "In session 2025-10-03 we found..." — too specific |
| Multi-language examples | One excellent example beats 5 mediocre ones |
Retrospective Mode
When /claudeception is invoked at session end:
- Review conversation for extractable knowledge
- List candidates with brief justifications
- Prioritize highest-value, most reusable (1-3 per session)
- Extract using the process above
- Summarize what was created and why
- Path Efficiency Analysis (enhanced retrospective):
Path Efficiency Analysis
After extracting skills, analyze the session's execution trajectory:
Path Efficiency Questions:
1. Recovery Rate: Did I recover from errors efficiently?
- Count error paths that led to correct recovery vs total errors
2. Repetitiveness: Did I repeat any failed approach?
- Flag any approach tried more than once without modification
3. Most Expensive Wrong Path: What was the costliest detour?
- Could it have been avoided with a Self-Critique Checkpoint?
- What early warning signal was missed?
4. Simulate-Before-Execute: Would pre-simulation have helped?
- Identify decisions where simulating 2-3 options would have saved time
If Path Efficiency Analysis reveals a systemic issue (not just a one-off mistake), extract it as a skill with trigger conditions for future sessions.
Self-Critique Checkpoint (Proactive Evolution)
Before executing any non-trivial step during a session, briefly evaluate:
1. "What is the theoretical basis for this approach?"
2. "What are the top 3 risks?"
3. "What signal would tell me this is wrong BEFORE I finish?"
After getting intermediate results:
4. "Did any predicted risks materialize?"
5. "Is the intermediate result consistent with expectations?"
6. "Should I continue or pivot?"
If a Self-Critique Catchpoint catches a risk that actually materialized, the insight is high-priority for extraction — it means the agent predicted correctly but didn't act on its own prediction.
Self-Check Prompts
After completing any significant task:
- "Did I just spend meaningful time investigating something?"
- "Would future-me benefit from having this documented?"
- "Was the solution non-obvious from documentation alone?"
- "Did I simulate before executing? If not, would it have helped?"
- "What was my most expensive wrong path this session?"
If yes to any of the first three, extract now.
Integration with Other Skills
| Skill | Relationship |
|---|---|
| three-layer-wisdom-extraction | Operates ABOVE claudeception — uses claudeception output as Layer 2 input |
| skill-refresh | Maintains claudeception-created skills over time |
| skill-creator | For formal eval/benchmark testing of skills |
| agent-nurture-framework | Provides the Part 9 enhancement context for Self-Critique and Path Efficiency |