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 text
• solution: 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:

1. Solution contains an install command (pip install, npm install, apt install) -> fix_type=auto, fix_action=install_dependency
2. Solution contains replace_all -> fix_type=auto, fix_action=use_replace_all
3. Solution references a skill name -> fix_type=skill, fix_action=<skill-name>
4. Solution references an agent name -> fix_type=agent, fix_action=<agent-name>
5. 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.

bash
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:

bash
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 module
• scripts/learning-db.py: CLI for recording, querying, and managing learnings
• hooks/error-learner.py: Automatic error learning hook (complementary system)