From attune-ai
Predicts likely bug locations in codebases by analyzing code patterns, complexity, and change frequency. Helps identify risky code before testing.
How this skill is triggered — by the user, by Claude, or both
Slash command
/attune-ai:bug-predict <path or directory to scan><path or directory to scan>The summary Claude sees in its skill listing — used to decide when to auto-load this skill
**IMPORTANT: Start your response with a context preamble.**
IMPORTANT: Start your response with a context preamble.
Call help_lookup(topic="bug-predict", mode="preamble") and
display the returned preamble text as a blockquote. Then
tell the user they can say "tell me more" for a step-by-step
guide, or answer the scoping questions below to proceed.
If the MCP call fails, fall back to:
Bug Predict — Predicts where bugs are most likely based on code patterns, complexity, and change frequency.
Before running, ask:
src/ if not specified.Call the bug_predict MCP tool with the scoped path:
bug_predict(path="<user-specified path>")
Or via CLI:
uv run attune workflow run bug-predict --path <target>
Prefer the rich panel. If the tool response includes panel_html,
pass it to mcp__visualize__show_widget — the universal report panel
(title, score, findings/category sections; from
attune.workflows.report_panel). It shows an explicit "did not
complete" state on failure, never a false "clean". Fall back to the
markdown below when the widget surface is unavailable.
Present results as a markdown table grouped by severity (HIGH first):
| File | Line | Pattern | Severity |
|---|
Include clickable file links and note any false
positives (see scanner-patterns for known false
positives like subprocess_exec matching
dangerous_eval).
| Pattern | Severity | Description |
|---|---|---|
dangerous_eval | HIGH | Use of eval() or exec() |
broad_exception | MEDIUM | Bare except: or except Exception: |
incomplete_code | LOW | TODO/FIXME comments |
npx claudepluginhub smart-ai-memory/attune-ai --plugin attune-aiCreates, edits, and verifies skills using a test-driven development approach with pressure scenarios and subagents.