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 skills add x0c/corral --skill docsgit clone --depth 1 https://github.com/x0c/corralWrote 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/x0c/corral/docs)<a href="https://agentmods.dev/skills/x0c/corral/docs"><img src="https://agentmods.dev/badge/skills/x0c/corral/docs/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/x0c/corral/docs"><img src="https://agentmods.dev/badge/skills/x0c/corral/docs.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00080 | $0.07997 |
| Opus 5.5 | $0.00032 | $0.03199 |
| Sonnet 5.5 | $0.00016 | $0.01599 |
| Haiku 4.5 | $0.00008 | $0.00800 |
Grade A, and why
corral 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 16d 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 — 317 lines — stays where its author put it; the contents beside it link to each section on GitHub.
corral:本地编程会话数据接口
corral 扫描本机 ~/.claude/projects/、~/.codex/sessions/、~/.kimi-code/sessions/、~/.cursor/chats/ 和 OpenCode 的
SQLite 数据库(~/.local/share/opencode/opencode.db,只读打开)下的会话历史,为大模型 Agent
提供结构化查询命令。这些命令只读、无副作用:不会拉起新会话、不会自动接续任务、不会修改
任何历史文件。拿到数据之后要做什么(继续任务、汇总给用户、转发给另一个 Agent)由调用方决定。
所有命令输出统一 JSON envelope,写到 stdout:
{"ok": true, "data": {...}, "error": null, "meta": {"version": 1}}
失败时 ok 为 false,data 为 null,error 包含:
code:程序可判断的错误分类(usage_error/not_found/ambiguous/history_unavailable/not_resumable/internal_error)message:人类可读的错误说明hint:建议的排查方向next_commands:可以直接执行的后续命令列表(绝对入口身份,照抄即可;不要改写成裸corral,以免命中 PATH 上另一份旧副本)
退出码:0 成功、1 一般失败(含未预料的 internal_error)、2 用法错误(参数不对,含未知 --fields)、3 会话不存在、5 会话标识有歧义。
不要只看 stdout 是否有内容来判断成功,检查退出码或 ok 字段。
安装与探针(CLI 缺失时不要停下问人)
调任何子命令前先确认入口与能力。corral 不在 PATH 上时,按下面可信源装到用户目录后继续原任务(用户要这个能力即已授权用户目录安装;sudo/系统目录/覆盖已装版本仍需显式授权):
- 可信源:
github.com/x0c/corral(corral包)。安装渠道三选一:pipx install corral、brew install x0c/corral/corral、源码树内bash cli/scripts/dev-install.sh(editable,供开发)。 - 装完先钉绝对路径并验能力(之后全会话只用该绝对路径;
next_commands给的即是):
CORRAL_BIN="$(command -v corral)" # 钉住本会话唯一入口
"$CORRAL_BIN" describe >/dev/null && echo describe-ok
"$CORRAL_BIN" diagnose | python3 -c "import json,sys; d=json.load(sys.stdin); assert d['ok']; print('diagnose-ok', d['data']['version'])"
diagnose的package_file/install_channel/stale_source_warning告诉你当前跑的是哪份拷贝;源码树里开发却加载了别处副本时会有告警,以告警为准换入口。diagnose的keepalive_server报告保活 tmux server 的调度档(priority、interactive_job、clamped)。clamped: true表示托管助手整体被 macOS 限流(新会话起得慢、同样的活比 Corral 外慢数倍);只能重启一次 server 解决,见docs/MAINTAINER_GUIDE.md「Keepalive server scheduling class」。- 源码树 editable 安装用于维护和开发,不会替代环境准备。跑耗时检查前先用
python3 scripts/dev_env.py check --repo .确认就绪(未就绪时跑python3 scripts/dev_env.py prepare --repo .;只诊断用doctor --repo . --json,命令经run --repo . -- <cmd>走 checkout.venv),再按 测试环境指南(python3 scripts/acceptance.py --json,只演练用--dry-run)做隔离验收;focused acceptance 不代替完整 UI/终端及干净安装门禁。安装/依赖失败应先按环境问题处理,不要误判成会话查询结果为空。
What ships with it
54 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.
- AGENT_TASK_ROUTING_GUIDE.md 24 KB
- AGENT_WORKFLOW_GUIDE.md 11 KB
- CROSS_RUNTIME_HANDOFF_KNOWLEDGE_BASE.md 24 KB
- design/AGENT_COMPLETION_NOTIFICATIONS_DESIGN.md 27 KB
- design/assets/web-task-butler-dark-preview.png 72 KB
- design/assets/web-task-butler-mobile-preview.png 117 KB
- design/assets/web-task-butler-preview.png 73 KB
- design/assets/web-task-butler-result-preview.png 91 KB
- design/MOBILE_REMOTE_DATA_PLANE_DESIGN.md 94 KB
- design/MOBILE_SESSION_ACTIONS_DESIGN.md 4.4 KB
- design/PI_SESSION_IDENTITY_EXTENSION_DESIGN.md 50 KB
- design/SESSION_TITLE_DESIGN.md 24 KB
- design/WEB_TASK_BUTLER_DESIGN.md 50 KB
- design/WINDOWS_COMPATIBILITY_DESIGN.md 7.9 KB
- DEVELOPMENT_ENVIRONMENT_GUIDE.md 9.3 KB
- EMBEDDED_TERMINAL_KNOWLEDGE_BASE.md 60 KB
- MAINTAINER_GUIDE.md 261 KB
- NEW_RUNTIME_ONBOARDING_KNOWLEDGE_BASE.md 28 KB
- OBSERVABILITY_KNOWLEDGE_BASE.md 17 KB
- PERFORMANCE_KNOWLEDGE_BASE.md 87 KB
- PROJECT_TASK_ROUTING_GUIDE.md 9.5 KB
- PUBLIC_PRESENTATION_GUIDE.md 8.5 KB
- REMOTE_KNOWLEDGE_BASE.md 110 KB
- reviews/NETWORK_UX_2026-09-10-review.md 8.5 KB
- screenshots/capture.py 23 KB runs code
- screenshots/corral-icon.png 70 KB
- screenshots/demo.gif 200 KB
- screenshots/emoji/1f347.png 972 B
- screenshots/emoji/1f348.png 1.1 KB
- screenshots/emoji/1f349.png 946 B
- screenshots/emoji/1f34a.png 777 B
- screenshots/emoji/1f34b.png 853 B
- screenshots/emoji/1f34c.png 1.1 KB
- screenshots/emoji/1f34d.png 1.1 KB
- screenshots/emoji/1f34e.png 782 B
- screenshots/emoji/1f350.png 597 B
- screenshots/emoji/1f351.png 953 B
- screenshots/emoji/1f352.png 787 B
- screenshots/emoji/1f353.png 1.0 KB
- screenshots/emoji/1f951.png 1.2 KB
- screenshots/emoji/1f95d.png 1.3 KB
- screenshots/emoji/1f965.png 1.3 KB
- screenshots/emoji/1f96d.png 1010 B
- screenshots/emoji/1fad0.png 1.1 KB
- screenshots/emoji/README.md 403 B
- screenshots/ios-chat.png 107 KB
- screenshots/ios-machines.png 41 KB
- screenshots/ios-sessions.png 104 KB
- screenshots/list.png 132 KB
- screenshots/search.png 140 KB
- screenshots/social-preview.png 224 KB
- SESSION_SCANNING_KNOWLEDGE_BASE.md 90 KB
- TERMINAL_UI_KNOWLEDGE_BASE.md 165 KB
- TEST_ENVIRONMENT_GUIDE.md 6.1 KB
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.
- 16d ago Changed e6f9804b01b9
- 24d ago Changed · +2 lines 2f7539b50e2b
- 29d ago Changed · +33 lines fda90746fa89
- 1mo ago Changed · +2 lines f77e4760b8b7
- 1mo ago Changed · +13 lines 2743f0ab4bff
- 1mo ago Changed 2310429b4cfc
- 1mo ago Changed 9456ce83d2d1
- 1mo ago Changed 5d73be59edde
- 1mo ago First seen · 267 lines · 80 tokens per session scan A d6b93bfebd37
corral is a skill published in the GitHub repository x0c/corral (3 stars, last pushed 16d ago), licensed MIT. It adds 80 tokens to every session and 7,997 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
memory-clean
Audit memory directory for structural issues (orphans, dangling refs, duplicates, missing sections, oversized entries) and staleness against session-history transcripts; report-first, fix-on-confirmation. Use when the user says "audit memory", "memory hygiene", or "find stale/duplicate memories".
memory-update
Scan agent's session-history transcripts for save-worthy signals (corrections, preferences, decisions, references), propose and write auto-memory files with valid frontmatter and MEMORY.md entry. Use when the user says "save this to memory", "remember that", or "scan this session for memories".
memory-sanitize
Produce share-safe copies of memory files under /tmp with PII redacted (paths, emails, session IDs, dates) and credentials scanned (tokens, keys); never mutates originals. Use when the user says "sanitize memory for sharing", "redact memory PII", or "scan memory for credentials".
vsearch
You CAN search other sessions' conversations. This is a full-text search across every vlx-term session on this machine or within one specified session, so a past discussion is findable even when nobody remembers which session held it. Use it whenever the user gestures at earlier work — "last time", "we solved this…
unifly
Inspect and manage UniFi Network infrastructure with the unifly CLI: devices, clients, VLANs, WiFi, firewall, VPN, switch ports, site settings, and cloud fleets. Use for UniFi network configuration, monitoring, and troubleshooting, not UniFi Protect camera or recording management.
neo4j-agent-memory-skill
Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model…