Imported from XoomitLXH/baoyan-radar-skill (SKILL.md). Install upstream with npx skills add XoomitLXH/baoyan-radar-skill. Copyright stays with the author.

Baoyan Radar Platform

Build a reusable 保研情报雷达 that can be published publicly while keeping each user's personal academic profile private.

Track both:

official admissions notices from graduate-school / college websites
community experience and evaluation pages such as Zhihu posts,经验贴,面经汇总, or lab-evaluation pages

Core rule

Do not hard-code the student's personal information into tracked files.

Keep public repository content limited to:

generic monitoring code
example config templates
extraction and scoring logic
local state schema

Keep private local content in untracked files such as:

config/profile.local.json
config/targets.local.json
.env
state/

Quick start

Fastest setup for a cloned repo:

Terminal wizard:

python3 scripts/setup_clone.py

Local web UI:

python3 scripts/setup_web.py

The setup flow will:

collect the user's private profile locally, including detailed project and competition experience
generate config/profile.local.json
generate config/targets.local.json from built-in school presets after auto-positioning the user into matching tiers
optionally install the daily scheduler for the current OS (macOS LaunchAgent or Windows Scheduled Task)
optionally run one immediate test scan / push

Manual setup from examples is still supported:

mkdir -p config state
cp references/profile.example.json config/profile.local.json
cp references/targets.example.json config/targets.local.json

Run one scan:

python3 scripts/baoyan_radar.py once \
  --profile config/profile.local.json \
  --targets config/targets.local.json \
  --db state/radar.db

Run continuous monitoring:

python3 scripts/baoyan_radar.py run \
  --profile config/profile.local.json \
  --targets config/targets.local.json \
  --db state/radar.db \
  --interval-min 60

Install a daily scheduled run with the cross-platform wrapper:

python3 scripts/install_daily_schedule.py \
  --profile config/profile.local.json \
  --targets config/targets.local.json \
  --db state/radar.db \
  --hour 9 --minute 0 \
  --push-mode digest \
  --send-empty-digest

On macOS this creates a LaunchAgent. On Windows it creates a Scheduled Task.

Run the local dashboard after building the frontend:

cd dashboard
npm install
npm run build
cd ..
python3 scripts/run_dashboard.py

Default address: http://127.0.0.1:8787/

Use the dashboard when the user needs a local platform view for filtering notices, tracking statuses, adding notes, seeing recent deadlines, or triggering scans interactively.

Analyze a single notice page:

python3 scripts/baoyan_radar.py inspect \
  --url https://example.edu.cn/notice/123

Score a mentor or lab page against the student's profile:

python3 scripts/baoyan_radar.py score \
  --url https://example.edu.cn/lab/pi

Generate a first-draft outreach email from local profile plus a mentor page:

python3 scripts/baoyan_radar.py draft-email \
  --url https://example.edu.cn/lab/pi \
  --mentor-name 张老师

What the bundled script does

`once`

load the private student profile and monitoring targets
fetch target pages
extract candidate links
separately handle official and experience sources
match admissions / experience keywords and profile keywords
deduplicate by URL in SQLite
summarize matched notices or experience pages
optionally push matched summaries to Feishu webhook
support either item push mode (one message per hit) or digest push mode (one daily summary)

`run`

Repeat once forever with a configurable interval.

`inspect`

Fetch one page and heuristically extract:

For official pages:

school / college / project title
absolute deadline text
required materials
assessment form
profile-fit score

For experience pages:

background / BG-like clues
interview or machine-test notes
suggestions and lab / school evaluation snippets
profile-fit score

`score`

Fetch one mentor or lab page and compute a transparent keyword-overlap fit score using:

target disciplines
research keywords
project keywords
student project summaries

`draft-email`

Produce a structured, reusable 套磁信草稿 using the private profile plus the fetched page context.

Privacy model

If the user wants to publish the project on GitHub, do this by default:

commit only example configs from references/
add config/*.local.json, .env, and state/ to .gitignore
never commit real student name, rank, GPA, phone, email, Feishu webhook, or target list unless the user explicitly wants that

Community-source note:

Prefer stable page URLs, collection pages, 专栏页, or manually curated post lists over fragile site-search result pages.
Large platforms like Zhihu may change HTML or add anti-bot protections, so keep community sources configurable and expect occasional source-specific tuning.
By default, experience sources are treated as single pages and do not follow page links. Set follow_links: true only for curated collection pages where link expansion is actually desired.

Tuning guidance

Prefer adding more precise target-page metadata before making the matcher more complex.

Useful tuning levers:

source-specific include keywords
source-specific exclude keywords
profile research keywords
project keywords and short project summaries
minimum fit score threshold
monitoring interval

References

Read these only when needed:

references/profile.example.json for the private profile schema
references/targets.example.json for target-page schema
references/presets.cn-cs.json for built-in CS school/source presets used by the clone-setup wizard
references/quickstart.md for clone-and-run onboarding
references/privacy-and-layout.md for repository layout and Git hygiene
references/scheduler.md for daily scheduling on macOS and Windows
references/preset-expansion.md for how to keep expanding school / college / lab / mentor presets

Files written by the script

SQLite DB path passed via --db
optional local logs if the user redirects output or runs under a scheduler

Keep state files local and untracked unless the user explicitly wants to archive them.

baoyan-radar

About