Run every audit at once and triage the findings into act-now / needs-a-look / dismissed buckets. Triggers on: run all audits, full sweep, audit everything, what should I fix, triage findings, discovery sweep, sweep the codebase.
Scanned 9/2/2026
Install to Claude Code
npx -y skills add Smart-AI-Memory/attune-ai --skill discovery-sweep --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: discovery-sweep
description: "Run every audit at once and triage the findings into act-now / needs-a-look / dismissed buckets. Triggers on: run all audits, full sweep, audit everything, what should I fix, triage findings, discovery sweep, sweep the codebase."
argument-hint: "<path or directory to sweep>"
---
# Discovery Sweep
**IMPORTANT: Start your response with a context preamble.**
Call `help_lookup(topic="discovery-sweep", 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:
> **Discovery Sweep** — Fans out across every audit source
> (pattern scan, bug-predict, security, dependencies, performance,
> docs, tests), dedups overlapping findings, and triages everything
> into three buckets so you know what to fix first.
This is the aggregate "what should I fix?" pass. For a single
focused audit, use the dedicated skill instead — `security-audit`,
`bug-predict`, `code-quality`, or `deep-review`.
## Scoping
Before running, ask:
1. **Target path**: "Which files or directory should I sweep?"
Default to `src/` if not specified.
2. **Speed vs. depth**: "Fast pattern-only sweep, or include the
LLM-backed sources?" (LLM sources cost budget; pattern-only is
free and quick.)
3. **Budget**: only if including LLM sources — "Spend cap? Default
is $10.00."
## Execution
Call the `discovery_sweep` MCP tool with the scoped path:
```
discovery_sweep(path="<user-specified path>")
```
Optional knobs:
```
discovery_sweep(path="src/", no_llm=true) # fast, free
discovery_sweep(path="src/", budget_usd=5.0) # cap LLM spend
```
Or via CLI:
```bash
uv run attune workflow run discovery-sweep --path <target>
```
## Output
The tool returns three buckets (`queue` / `questions` / `rejected`),
the run `metadata`, and a pre-rendered **`board_html`**.
**Prefer the rich triage board.** Pass the response's `board_html`
straight to `mcp__visualize__show_widget` — it renders the three
buckets as a triage board (severity-coloured queue cards with
`file:line` + source + confidence; questions with `reason`/`next_step`;
rejected collapsed under a `<details>`; a footer with spend/budget,
sources that ran, failures, and duration). The HTML is display-only and
injection-safe (generated by `attune.workflows.discovery_sweep.board`).
**Fall back to markdown** when the widget surface is unavailable —
present each bucket as its own section:
- **Queue** — table grouped by severity (critical first) with
clickable file links.
- **Questions** — each finding's `reason` and `next_step`.
- **Rejected** — summarize the count; expand only if asked.
Either way, close with the run metadata: spend vs. budget, sources that
ran, any source failures, and duration.
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