Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/arcblock/agent-skills/issue-sweepnpx skills add ArcBlock/agent-skills --skill issue-sweepgit clone --depth 1 https://github.com/ArcBlock/agent-skillsWrote 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/arcblock/agent-skills/issue-sweep)<a href="https://agentmods.dev/skills/arcblock/agent-skills/issue-sweep"><img src="https://agentmods.dev/badge/skills/arcblock/agent-skills/issue-sweep.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00179 | $0.20677 |
| Opus 5 | $0.00089 | $0.10339 |
| Sonnet 5 | $0.00036 | $0.04135 |
| Haiku 4.5 | $0.00018 | $0.02068 |
Grade A, and why
issue-sweep scanned grade A with 1 finding 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 4d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
concrete check for the specific capability (`curl` the live endpoint, launch the headless browser How it starts
The opening of the file, as written. The whole thing — 818 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Sweep — batch-process issues with new human replies
Repo profile — read
.claude/repo-profile.mdfirst. This skill is repo-agnostic; arc is the reference implementation. Use the profile's values wherever this doc shows an arc default:repo_slug(thegh -R <owner/repo>target),gate_mode(arc =scripts: no CI on PRs),verification_entry/pre_merge_entry(gate commands),kb_issue, the UI Face Paths, andplugin_root(where issue-graph's scripts live). Arc's own provenance for the lessons below (issue numbers, war-stories) is not inlined here (fuller case narratives, where they exist, are under.claude/case-law/).
A batch driver over issue-review. issue-review
handles ONE issue (read → verify against code → act). issue-sweep finds which
issues need handling right now — the ones whose latest comment is a human reply
the agent hasn't acted on yet — and runs the per-issue engine on each.
This is the thing a cron should schedule: one run = scan + process a batch. In
environments without a working scheduler, run it by hand: /agentloop:issue-sweep.
★ 无人值守铁律(cron routine / /loop——本 skill 的默认运行形态):绝不调用任何会等待用户的工具——
AskUserQuestion、Workflow(需交互式 opt-in 确认)、EnterPlanMode(退出需用户批准)。 无人应答 → 整条 routine 永久挂死(实测:sweep routine 整夜卡在「是否运行 workflow」的提问上)。需要人拍板的问题,照design-reviewAutonomous escalation 范式处理:把选项 + 你的推荐 + 被 block 的内容作为 comment(挂needs-human-confirm)落到对应 issue,然后继续处理下一项。禁止的是会等待确认的交互式编排,不是并行本身:无人值守环境优先使用无需 opt-in、不会等待用户的 subagent/agent fan-out;若当前 runtime 没有这种能力才串行 inline fallback。repo hook(.claude/hooks/deny-interactive-unattended.py)会在无人值守 session 硬 deny 这三个工具兜底——被 deny 即说明你在无人值守环境,按本条纪律走,不要重试。
输出语言与写作规范(中文,信雅达),同
issue-review。 所有面向团队的产出——issue/PR comment、PR/issue 描述正文、issue 标题、triage 说明——一律中文;代码标识符、路径、命令、path:line、测试输出保持原样。PR 与 commit 标题必须全英文——完整 Conventional Commits(type(scope): english description,冒号后的描述也用英文),不得留中文;issue 标题保持中文。不堆砌:先一句话结论,再最少但足够的证据(文档 / 代码path:line/ 真实测试输出,UI 相关必附截图);长日志折叠进<details>。
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- test/sweep-golden/fixtures/1025-forbidden-auto-merge.json 806 B
- test/sweep-golden/fixtures/1362-presence-heartbeat-incidental-deferral-word.json 2.3 KB
- test/sweep-golden/fixtures/1863-audit-suggestion-list-not-instruction.json 2.5 KB
- test/sweep-golden/fixtures/2271-test-sweep-finding-zero-comment.json 1.7 KB
- test/sweep-golden/fixtures/328-early-human-reply-sank.json 957 B
- test/sweep-golden/fixtures/3432-nightly-test-report-zero-comment.json 1.8 KB
- test/sweep-golden/fixtures/367-body-only-no-labels.json 942 B
- test/sweep-golden/fixtures/533-non-terminal-deferred.json 1.1 KB
- test/sweep-golden/fixtures/535-false-premise-trap.json 1.2 KB
- test/sweep-golden/fixtures/869-research-no-labels.json 887 B
- test/sweep-golden/golden.test.ts 34 KB runs code
- test/sweep-golden/lib.ts 15 KB runs code
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.
- 4d ago First seen · 818 lines · 179 tokens per session scan A dbeae037246f
issue-sweep is a skill published in the GitHub repository ArcBlock/agent-skills (5 stars, last pushed 3d ago), licensed MIT. It adds 179 tokens to every session and 20,677 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…