majiayu000 Learn Error Pattern Skill
Parse a user-provided "error -> solution" pair, classify it, store it in the cross-session learning database at high confidence, and confirm back. One pattern per invocation. All database operations go through the learning-db.py CLI.
Instructions
Step 1: Parse Input
Extract two fields from the user's input:
error_pattern: The error message or symptom textsolution: The fix or resolution text
Accepted input formats:
/learn "error pattern" -> "solution"/learn "error pattern" => "solution"- Freeform: "teach that X means Y" or "remember: when X, do Y"
Both fields must be non-empty. If either is missing, ask the user for the missing part before proceeding. If the error pattern is vague (e.g., "it broke") or the solution is non-actionable (e.g., "fix it"), ask the user to provide the specific error message and concrete fix steps — vague patterns fail to match future errors and waste database space.
Step 2: Classify Fix Type
Determine fix_type and fix_action from the solution text by applying these rules in order:
- Solution contains an install command (
pip install,npm install,apt install) ->fix_type=auto,fix_action=install_dependency - Solution contains
replace_all->fix_type=auto,fix_action=use_replace_all - Solution references a skill name ->
fix_type=skill,fix_action=<skill-name> - Solution references an agent name ->
fix_type=agent,fix_action=<agent-name> - Otherwise ->
fix_type=manual,fix_action=apply_suggestion
Step 3: Store Pattern
Execute the learning-db.py CLI to persist the pattern. Always pass user-provided strings as CLI arguments exactly as shown — never inline them into Python code via f-strings or string concatenation, because quotes or special characters in error text will break the script and create injection risk.
python3 ~/.claude/scripts/learning-db.py record \
"<error_type>" \
"<error_signature>" \
"<error_pattern> → <solution>" \
--category error \
--confidence 0.9
<error_type>: The classified type (e.g., "missing_file", "multiple_matches")<error_signature>: A kebab-case key derived from the error pattern- Confidence is always 0.9 for manually taught patterns. If the pattern already exists, this updates its confidence to 0.9.
Example:
python3 ~/.claude/scripts/learning-db.py record \
"multiple_matches" \
"edit-tool-multiple-matches" \
"Edit tool fails with 'found N matches' → Use replace_all=True parameter" \
--category error \
--confidence 0.9
The script must exit 0 and print confirmation. If it fails, see Error Handling below.
Step 4: Confirm to User
Always display what was stored so the user can verify correctness — silently storing without confirmation hides typos and misclassifications:
Learned pattern:
Error: "<error_pattern>"
Solution: "<solution>"
Type: <fix_type> (<fix_action>)
Confidence: 0.9
Error Handling
Error: "Script fails with ImportError or FileNotFoundError"
Cause: scripts/learning-db.py not found or not synced to /.claude/scripts/
Solution: Verify working directory is the repo root, or use `/.claude/scripts/learning-db.py` for cross-repo access.
Error: "Database locked"
Cause: Another process holds the SQLite lock
Solution: Retry after 2 seconds. If persistent, check for hung processes with lsof ~/.claude/learning/learning.db.
Error: "User provides only error, no solution"
Cause: Incomplete input Solution: Ask the user explicitly for the solution text. Do not guess or fabricate solutions.
References
hooks/lib/learning_db_v2.py: Unified learning database modulescripts/learning-db.py: CLI for recording, querying, and managing learningshooks/error-learner.py: Automatic error learning hook (complementary system)