Borrowing it
Nothing to install: this file belongs to Smart-AI-Memory/attune-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Smart-AI-Memory/attune-ai/main/.agents/skills/discovery-sweep/SKILL.mdgit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/smart-ai-memory/attune-ai/discovery-sweep)<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/discovery-sweep"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/discovery-sweep/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/discovery-sweep"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/discovery-sweep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.00714 |
| Opus 5 | $0.00028 | $0.00357 |
| Sonnet 5 | $0.00011 | $0.00143 |
| Haiku 4.5 | $0.00006 | $0.00071 |
Grade A, and why
discovery-sweep scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Target path: "Which files or directory should I sweep?"
Default to
src/if not specified. - Speed vs. depth: "Fast pattern-only sweep, or include the LLM-backed sources?" (LLM sources cost budget; pattern-only is free and quick.)
- 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:
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).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 81 lines · 57 tokens per session scan A 0f6fa8fb05c4
discovery-sweep is a skill published in the GitHub repository Smart-AI-Memory/attune-ai (10 stars, last pushed today), licensed Apache-2.0. It adds 57 tokens to every session and 714 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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