Gemini 4：把视频教程转化为生产级 RL 工程技能

Watch the YouTube videos listed below and turn their methods and insights into reusable,
file-based agent skills. Then publish the results as a well-designed HTML report served from a
local web server.

Videos:
- https://www.youtube.com/watch?v=zt-U4lDdenY&t=1559s
- https://www.youtube.com/watch?v=jRCpXUjz4CI
- https://www.youtube.com/watch?v=5wo0mLlG0fk&t=1815s

Use only these videos as primary sources. Do not search for, substitute, or add other videos.
You may consult official documentation and the code repositories the videos link to or name,
but only to check or update details shown in the videos.

## Focus
Extract practical, repeatable methods. Where the videos cover them, include:
- Defining tasks, observations, actions, and termination conditions.
- Creating reproducible environments and reset behaviour.
- Building verifiers and reward functions.
- Detecting reward hacking and invalid successes.
- Collecting, scoring, and reviewing agent trajectories.
- Preparing data for agent training and evaluation.
Do not invent coverage for areas the videos don't address. List those areas as gaps instead.

Capture insights, not only procedures. For every method, record why the speakers use it, what
went wrong before they did, any results or numbers they reported, and the warnings they gave.
Treat named tools and frameworks (for example Harbor or Terminal-Bench) as one implementation
of a method. Record the underlying principle first and the tool-specific steps second.

## Permissions and limits
- Allowed: reading the video pages; downloading subtitles, transcripts, and the video files
  for analysis; extracting frames; reading official docs and repositories named in the
  videos; creating a local virtual environment and installing open-source packages into it;
  running a local static web server bound to 127.0.0.1.
- Docker: [allowed / not allowed].
- Not allowed: model training, GPU jobs, paid hosted services, calls to paid LLM APIs, or
  publishing anything to a public host. For trajectory collection tests, use a scripted,
  rule-based, or random policy.
- Keep all files inside ./rl-env-skills/ in the current working directory. Delete downloaded
  video files when finished.
- Do not ask for confirmation for the actions allowed above. Stop and report only if a required
  action falls outside them.
- If a video is private, removed, region-locked, or has no usable audio or transcript, say so in
  the report. Continue with the remaining videos, and do not replace it.

## Capability honesty
Before starting, record which relevant capabilities you have: video viewing, frame
extraction, transcript access, code execution, browser preview. If you cannot inspect video
visuals, work from transcripts and mark every visual claim as "not observed".
Never describe on-screen content you did not actually see.

## Analysis
For each video:
- Record title, channel, published date, duration, and view count with the date checked.
- Watch it in full. Inspect visual demonstrations, code, diagrams, and narration where your
  capabilities allow.
- Build a timestamped outline of the main sections.
- Preserve timestamps for every important procedure, decision, and warning.
- List the key insights and takeaways: design decisions and the reasons behind them, lessons
  learned, failure stories, reported results, and warnings. Give each one a timestamp.
- Label each claim OBSERVED (seen or heard in the video), INFERRED (your interpretation), or
  UPDATED (adapted to current official docs; cite the doc URL and the date checked).
- Re-inspect unclear moments. Record unresolved details instead of guessing.
- Paraphrase. Quotes must be under 15 words and carry a timestamp. Do not reproduce transcripts.
Then note where the videos agree, where they differ, and where one fills a gap in another.

## Skills
Create 2–4 skills grouped by practical capability. A skill may draw from several videos.
Layout:

rl-env-skills/
  <skill-name>/
    SKILL.md          # required
    evidence.md       # timestamps, OBSERVED/INFERRED/UPDATED labels, source per point
    scripts/ templates/   # only if they materially help; scripts must be tested
  report/
    index.html        # the HTML report (see below)
    assets/           # extracted frames or screenshots used in the report, if any
  REPORT.md
  manifest.json

SKILL.md must begin with YAML frontmatter:
---
name: <kebab-case, matches folder name>
description: <1–2 sentences: what it does and when to use it, including trigger phrases>
---
The body must cover: prerequisites, required inputs, key insights (the principles and lessons
behind the procedure, each with its source and timestamp), procedure (numbered steps), expected
outputs, decision rules, failure recovery, observable success checks, and a tool-agnostic path
for projects that don't use the tools shown in the videos. Another agent should be able to
apply it to a new project without watching the videos.

A skill must not be only a wrapper around one tool's features. If you removed every
tool-specific command, the insights and decision rules should still stand on their own.

Keep these four things distinct throughout: task datasets, recorded trajectories, supervised
training examples, and RL policy optimisation. Never imply that exporting trajectories is RL
training.

## Validation
- Structural: frontmatter is present and valid, the name matches the folder, every referenced
  file exists, every evidence timestamp falls within the source video's duration, and every
  skill has a key insights section with at least three sourced insights.
- Execution: run every script you created. Where feasible, apply at least one skill end to end on
  a small example the videos don't use (e.g. a text grid-world, or a "make this failing unit
  test pass" task).
- Report each item as STRUCTURALLY CHECKED, EXECUTED (with command and result), or UNTESTED
  (with reason).

## HTML report (report/index.html)

### Content, in this order
1. Header: report title, date checked, the agent's capability summary (had / lacked).
2. Summary: 3–5 sentences on what was produced and the main limitations. No marketing language.
3. Sources: one row per video (title, channel, published date, duration, views with date
   checked, linked code or repository, access status).
4. One section per video:
   - The original video embedded with a privacy-enhanced YouTube embed
     (https://www.youtube-nocookie.com/embed/<VIDEO_ID>), with a 16:9 responsive frame.
   - Link to the original.
   - Timestamped outline of the video.
   - Key insights and takeaways, each with a clickable timestamp and a label.
   - Learning points: numbered, each with a clickable timestamp that seeks the embedded player
     (YouTube IFrame Player API) and an OBSERVED / INFERRED / UPDATED label.
   - Unresolved details.
   - If embedding is blocked, show the video thumbnail linked to youtube.com/watch?v=<ID>
     and state that embedding was unavailable.
5. How the videos compare: agreements, differences, and how each fills the others' gaps.
6. One section per skill:
   - Name, absolute path to SKILL.md, one-sentence purpose, and when to use it.
   - Key insights the skill carries, with their sources.
   - Procedure summary (the key steps, not the whole file).
   - Evidence: every supporting source point as a row — video, timestamp (clickable), label,
     and what the skill took from it. Include doc URLs for UPDATED items.
   - Validation status with the command and result if executed.
7. Coverage matrix: skills × the videos, showing which video supports which skill, plus
   the focus areas no video covered.
8. Data distinctions: a short table defining task dataset, trajectory, supervised example, and
   RL policy optimisation, and which of them the skills actually produce.
9. Limitations and untested items.
10. Three copy-paste reuse requests, each in a code block with a copy button.

### Visual design: light mode, in Google's design language
- Style only: do not use Google logos or wordmarks, or suggest the report is a Google product.
- Colours: page #FFFFFF; secondary surfaces #F8F9FA; borders #DADCE0; primary text #202124;
  secondary text #5F6368; links and primary actions #1A73E8. Use the Google accent set
  (blue #4285F4, red #EA4335, yellow #FBBC04, green #34A853) only for status and labels,
  e.g. OBSERVED blue, INFERRED yellow tint with dark text, UPDATED green, UNTESTED red.
  Text must meet WCAG AA contrast.
- Type: Google Sans / Google Sans Text if available from Google Fonts, otherwise Roboto; Roboto
  Mono or Google Sans Code for code and timestamps. Clear scale: one H1, H2 per section, 16px
  body, 1.5 line height, comfortable line length (about 70–80 characters).
- Layout: 8px spacing grid, max content width about 1100px, sticky left table of contents on
  desktop that collapses on mobile, flat cards with 1px borders or a subtle Material elevation
  shadow, 8–12px corner radius, plain tables with hairline row dividers.
- Avoid the generic "AI-generated" look: no gradients, purple/indigo themes, glassmorphism,
  glowing effects, emoji used as icons, sparkle icons, oversized hero banners, decorative
  blobs, or hype copy ("unlock", "supercharge", "seamless"). Prefer real content, tables, and
  data over decoration.
- Technical: one self-contained HTML file with inline CSS and JS. Only external requests allowed:
  Google Fonts and YouTube embed/API. Responsive down to 360px width with no horizontal
  page scroll. Semantic HTML, keyboard-accessible controls, alt text on images.

### Serve and verify
1. Start a static server from rl-env-skills/report/ bound to 127.0.0.1, e.g.
   `python3 -m http.server 8000 --bind 127.0.0.1`. If port 8000 is busy, use the next free port.
   Keep the server running when you finish.
2. Do not rely on opening the file from disk: YouTube embeds often fail without an http origin.
3. Verify: the page returns HTTP 200; every embed loads or shows the fallback; timestamp links
   seek the player; all anchors and file links resolve; no console errors. If you have a browser
   tool, take a desktop and a 375px-wide screenshot and save them to report/assets/.
4. Report how you verified each item, and which checks you could not run.

## Deliverables
- REPORT.md: a plain-text version of the report's findings.
- manifest.json:
  { "date_checked": "...",
    "videos": [{"url","title","channel","published","duration","views","access_status"}],
    "skills": [{"name","path","sources":["<video id>", "..."],
                "insights": <number of sourced insights>,
                "validation":"executed|structural|untested"}],
    "capabilities": {"video": bool, "frames": bool, "transcripts": bool, "code_exec": bool,
                     "browser_preview": bool},
    "report": {"path": "<absolute path to index.html>", "preview_url": "http://127.0.0.1:<port>/",
               "server_command": "...", "verified": [...], "not_verified": [...]} }
- Final reply: the preview link on its own line first
  (http://127.0.0.1:<port>/), then the command to restart the server if it stops, then a short
  summary of the videos, skills with absolute paths, validation results, and limitations.

Use rl-env-skills/verifier-and-anti-cheat-auditor/SKILL.md to design a verifier
for my "make this failing unit test pass" task. Flag any way an agent could
pass without fixing the bug.