通过检查代码、目标和限制,评估新的或现有的 AI 项目;核对可用目录中的技能后提出建议,并制定包含依赖关系、完成条件和检查方式的任务清单与实施计划。
- 规划新 AI 项目的启动
- 梳理现有项目的技术问题
- 从可用目录中挑选技能
使用 Beatra 付费服务修整现有的人工智能人像、商品图和广告视觉,处理不自然的皮肤、变形的手脸、重复纹理及光照不一致。
Install and use the official AI Image Realism package, pinned by digest, for paid hosted work on the Beatra service.
实测评分 未实测
查看安装方法本技能介绍如何使用 Beatra 付费服务修整现有人工智能图像中的不自然细节。处理对象包括塑料感皮肤、变形的手或脸、反复出现的纹理以及不一致的光照。提供的资料说明了安装指定版本的软件包、授权、确认费用、提交图像和查看结果的步骤。
如果您想让人工智能生成的人像、商品图或广告视觉看起来更自然,可以使用本技能。修整后,请将结果与原图按原始尺寸对照,确认人物身份、文字、标志和商品形状没有发生变化。
此目录条目仅提供操作指引,不含可执行软件包。您需要另行安装并授权。流程要求 Python 3.10 或更高版本、Beatra 账号、网络连接和预付额度。每次付费处理前,都需要确认费用和要发送的文件。图像和指令会发送给 Beatra 及外部模型提供方。实际费用可能高于估算,发布前需要由人工检查结果。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 根据 SKILL.md 的内容检查了需要注意的操作和使用前提。评分只依据实际运行的结果(实测对比)。
不计入评分。 由 AI(OpenAI Codex)于 2026年10月25日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
Make existing AI portraits, product images, and marketing visuals look more natural by fixing plastic skin, malformed hands and faces, repeated textures, and inconsistent lighting, from inside Claude Code, Codex, or OpenClaw. The work is produced on the hosted, paid Beatra service
(mcp.beatra.ai).
This catalog entry is a reviewed pointer, not the executable package. It contains no client code and performs no Beatra operation by itself. The package it points to bundles three standard-library Python scripts that make network calls and store a credential. It has no self-updater: it never downloads code and never changes its own files. Read Install and Security & Safety Notes before activating it.
| Pinned identity | Value |
|---|---|
| Package | ai-image-realism 0.3.6 |
| Source commit | beatra-ai/ai-image-realism-skill@0b29222 (full SHA 0b29222a61c52917887efa40d39e100683b03625) |
| Package tree | d7b1f2866555aae60d53de35293437b2ecbdb6d6 (Git tree ID of skills/ai-image-realism at that commit) |
| License | MIT No Attribution (MIT-0) |
The pinned tree holds regular files only (no symlinks, no executable bits, no binaries). Confirm that with the inspect commands below.
Explain that this fetches an external package from github.com, then ask
for approval. Only after approval:
set -e
umask 077
review_dir="$(mktemp -d)"
cd "$review_dir" || exit 1
git clone --quiet --filter=blob:none --no-checkout https://github.com/beatra-ai/ai-image-realism-skill.git src
git -C src checkout --quiet 0b29222a61c52917887efa40d39e100683b03625 -- skills/ai-image-realism
test "$(git -C src rev-parse '0b29222a61c52917887efa40d39e100683b03625:skills/ai-image-realism')" = 'd7b1f2866555aae60d53de35293437b2ecbdb6d6' && echo TREE-OK
Stop if the last command does not print TREE-OK. The expected commit and
tree IDs come from this catalog entry, not from the package or its website,
and Git addresses every fetched object by its hash.
git -C src ls-tree -r 0b29222a61c52917887efa40d39e100683b03625 -- skills/ai-image-realism | awk '$1 != "100644"' # expect no output
grep -n -E 'maybe_auto_update|download_update|_apply_update' src/skills/ai-image-realism/scripts/mcp_client.py # expect no output
The first check lists any symlink, executable, or submodule entry; the second finds the entry points of the self-updater that earlier versions shipped.
Report to the user what the review found: SKILL.md, manifest.json,
install-source.json (the one-line marker {"schema_version":1,"source":"github"}),
the bundled Markdown references, and scripts/authorize.py,
scripts/mcp_client.py, scripts/uninstall.py (Python 3.10+, standard
library only, no dependency installation, no lifecycle hooks). Summarize the
network, credential, local-state, telemetry, and version-notice behavior
listed under Security & Safety Notes.
The manifest and pattern scans above are only inventory aids. Before activation, read every file in the pinned tree, including each script, reference and hook, and inspect all dependency and network behavior. Do not claim source review from grep output. If complete review cannot be performed, stop before copying or executing; ask for a separate review rather than treating a digest as evidence of safety.
Ask for a second, separate approval, because this changes agent configuration. Then copy the reviewed tree to the host's skills directory (Claude Code shown; use the equivalent path for other hosts):
dest="$HOME/.claude/skills/ai-image-realism"
test ! -e "$dest" && test ! -L "$dest" || { echo "destination exists; stop and ask the user"; exit 1; }
cp -R "src/skills/ai-image-realism" "$dest"
There is no update switch to turn off in this version.
Authorization opens a browser sign-in and stores a bearer token. Ask first.
python3 "$dest/scripts/authorize.py"
Start a new agent session if the host discovers skills only at startup.
SKILL.md and its references; they define the
routes, payload shapes, and review loop.beatra.models.list, task list) to
read current model cards and credit estimates.client_request_id and poll the returned task.billing.net_charged_credits, review the
result with the user, and ask again before any further paid stage.python3 "$HOME/.claude/skills/ai-image-realism/scripts/mcp_client.py" verify
After the user approved the cost card for this exact payload, submit once with
a stable request id using the tool name from the installed SKILL.md. If the
response is lost, retry only with the same client_request_id and identical
arguments; never submit a replacement while the task is running.
This pinned version cannot update itself: it has no code that downloads or
replaces package files. update only reports what the store or tool that
installed the copy offers; for a copy installed by the steps above it finds
no such record and says to update the way you installed it. See the version
notice under Security & Safety Notes.
To move to a newer version, repeat Steps 1 to 3 for that commit in a new
review directory, show the user the file-level changes against the installed
copy (for example git -C src diff --stat 0b29222 <new-commit> -- skills/ai-image-realism),
and replace the directory only after approval. A version not listed in this
entry has not been reviewed here.
dest="$HOME/.claude/skills/ai-image-realism"
python3 "$dest/scripts/uninstall.py" --dry-run
python3 "$dest/scripts/uninstall.py"
Read the JSON decision. disconnected means this was the last Beatra skill on
the device: the token was revoked (when revoked is true), the files
credentials.json, installation.json, host.json, skills.json,
network.json, balance-holds.json, pending_authorization.json, and
.authorize.lock under ~/.beatra were removed, and so were the records
state.json, source.json, registration.json, and update.lock in each
~/.beatra/updates/<id>/. A directory is removed only once it is empty; the
script never deletes recursively and leaves anything it does not know, such
as an interrupted update's transaction/. keep_connection means another
Beatra skill still uses the shared token, which stays. revoke_retry means
the server refused the revocation and nothing changed; run it again shortly.
The script never deletes the package directory: after confirming the path it
prints with the user, delete $dest.
If anything is still under ~/.beatra after a disconnected decision, show
it to the user before removing it. A token that could not be revoked expires
after 15 days idle and can be revoked at once in the Beatra Console under
Agents.
~/.beatra/credentials.json.risk: critical is deliberate.
api.beatra.ai and tool calls go to mcp.beatra.ai; an upload is a
PUT of the approved file to the HTTPS address the server returns, without
the bearer token. Unless a cached decision is still valid (10 minutes to 7
days), each run first asks https://api.beatra.ai/skill-route, and also
https://api.beatra.cn/skill-route when the first is slow or answers cn,
which network to use. These probes carry no credential or package identity.
When the answer is cn, authorization and tool calls go to api.beatra.cn
and mcp.beatra.cn with the same bearer token, and the version notice
reads from www.beatra.cn; Beatra serves these .cn hosts from mainland
China.
python3 "$dest/scripts/mcp_client.py" network --use ai pins the .ai
hosts and stops the probes; after that no request, the version-notice read
included, retries on the other network.verify, tools, upload, or call, at most once every 24 hours
per install path, it records the check time and looks, read-only, for
records another installer left beside the package (ClawHub origin.json,
SkillHub or skills CLI lock entries, gh skill frontmatter). A copy
installed by the steps above normally has none and makes no request. When
one is found, it reads https://beatra.ai/skills/ai-image-realism/sources.json
(no credential, 3-second timeout, 64 KiB cap) and, if that installer offers a
newer version, prints one line to stderr with the installer's own update
command. It never runs the command.cn route (see Network)
requests reach it through Beatra's hosts in mainland China. See the
privacy policy.authorize.py runs an OAuth device flow, opens the browser
on the approval page, and stores one bearer token in
~/.beatra/credentials.json (directory 0700, file 0600). The token never
goes into argv, environment variables, or output. It carries full Beatra
scope, including wallet:spend, has a sliding 15-day idle lifetime, and is
shared by every Beatra skill on the device. While the user has not yet
selected Allow, the pending device code is kept in
~/.beatra/pending_authorization.json (0600) so a rerun finishes the same
approval instead of opening a new one. When verify, tools, upload, or
call finds no working token, the client makes one check of that saved
approval and stores the token if the user has already selected Allow; it
never starts a new authorization or opens a browser.~/.beatra/ also holds installation.json (random
installation reference), host.json (agent platform and hostname),
skills.json (installed Beatra skill paths), network.json (route
decision, with a country code when the route answer includes one),
balance-holds.json (after an insufficient-credit refusal, that tool is
answered locally for up to 30 minutes; call --recheck-balance sends one
call), a short-lived .authorize.lock, and, per install path,
updates/<id>/state.json (last version check), source.json (install
source), and registration.json (last registration). All of these files
are 0600.claude-code, from environment variables), and the installation reference.
After connecting, at most once a day unless the version, platform, route, or
install source changes, the client makes a non-billable
beatra.installations.register call with those package and platform
fields, the release channel, the network route, the install source
(github for this tree), and coarse runtime facts: OS and OS version, CPU
architecture, Python version, locale, and UTC offset. Every tool call adds
source_package_slug and source_platform. There is no opt-out for these
fields.Compare the result with the source at full size for identity, text, logo geometry, product shape and any new artifact before claiming the defect is fixed, and report visible drift instead of hiding it.
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通过检查代码、目标和限制,评估新的或现有的 AI 项目;核对可用目录中的技能后提出建议,并制定包含依赖关系、完成条件和检查方式的任务清单与实施计划。
检查代码和基础设施中的安全风险,梳理潜在威胁并规划防护改进。涵盖 OWASP 检查、攻击与防御验证及事件响应;操作范围需获明确授权,并结合具体环境验证结果。
将重复任务整理成 Andru.ia 技能,明确输入、输出、示例和限制;编写 `SKILL.md`,检查目录结构和验证要求,并准备供审阅的拉取请求。
项目行业确定后,整理行业背景、可能适用的法规、用户熟悉的界面模式、技术选择和风险,并区分已核实事实、假设及待专家确认的事项。
通过 2slides API 将文本、提纲或文档制作为演示稿,可参考图片风格、选择主题、添加人工智能旁白,并导出幻灯片图片或音频。
规划360度评估或同伴反馈流程,并根据已确认的需求创建空白答复台账。可生成字段一致的CSV、SQL、JSON Schema或Notion属性映射,并将可见范围与评分配置分开整理。