HyperFrames Media Use
heygen-com/hyperframes
Finds, generates and edits media for HyperFrames video projects: music, sound effects, images, icons, logos, voiceovers, captions and color grades.
A complete methodology for turning scanned or image-based learning materials into high-quality desktop, web, or mobile practice products.
Install Scan To Practice
$ npx skills add parz0val0/scan-to-practice --skill scan-to-practice -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install parz0val0/scan-to-practice scan-to-practice --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
Claude Code skills documentation · loads skills from .claude/skills/
Install the "scan-to-practice" agent skill from https://github.com/parz0val0/scan-to-practice/tree/main into .claude/skills/scan-to-practice/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scan-to-practice", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add parz0val0/scan-to-practice --skill scan-to-practice -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install parz0val0/scan-to-practice scan-to-practice --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "scan-to-practice" agent skill from https://github.com/parz0val0/scan-to-practice/tree/main into .agents/skills/scan-to-practice/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scan-to-practice", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add parz0val0/scan-to-practice --skill scan-to-practice -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install parz0val0/scan-to-practice scan-to-practice --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "scan-to-practice" agent skill from https://github.com/parz0val0/scan-to-practice/tree/main into .cursor/skills/scan-to-practice/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scan-to-practice", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add parz0val0/scan-to-practice --skill scan-to-practice -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install parz0val0/scan-to-practice scan-to-practice --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "scan-to-practice" agent skill from https://github.com/parz0val0/scan-to-practice/tree/main into .gemini/skills/scan-to-practice/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scan-to-practice", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install parz0val0/scan-to-practice scan-to-practiceInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add parz0val0/scan-to-practice --skill scan-to-practice -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "scan-to-practice" agent skill from https://github.com/parz0val0/scan-to-practice/tree/main into .github/skills/scan-to-practice/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scan-to-practice", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add parz0val0/scan-to-practice --skill scan-to-practice -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install parz0val0/scan-to-practice scan-to-practice --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "scan-to-practice" agent skill from https://github.com/parz0val0/scan-to-practice/tree/main into .opencode/skills/scan-to-practice/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scan-to-practice", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
scan-to-practice- GitHub stars
- 109
- Token cost
- ~2k tokens
- SKILL.md length
- 942 words
- Files
- 12
- Skills in repo
- 1
- Repo updated
- First seen
- Licence
- MIT
At a glance
A complete methodology for turning scanned or image-based learning materials into high-quality desktop, web, or mobile practice products.
- Works in 5 steps: Source fidelity over AI invention.… → Fix data before presentation. Correct… → Validate the complete dataset. Sampling… → …
- A user wants to convert scanned exercises
- SKILL.md covers When to use this skill, Core principles, Nine-stage pipeline and Quick decision card, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Scan To Practice is an agent skill from parz0val0/scan-to-practice. A complete methodology for turning scanned or image-based learning materials into high-quality desktop, web, or mobile practice products. Covers visual transcription, data assembly, answer-key-driven controls and grading, product design, animation, validation, and long-term maintenance. Use when a user wants to convert scanned exercises, workbook pages, or question-bank photos into an interactive practice application, including typed answer controls, persistent attempts, and mistake review.
Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files (for example `README.md`, `docs/01-project-journey.md` and `docs/02-troubleshooting.md`).
It sits in Media & Creative, covering Transcription and Quizzes and assessments. The repository describes itself as: Scan-to-Practice: a field-tested AI skill and methodology for turning scanned learning materials into structured practice products. The licence is MIT.
When your agent uses it
- A user wants to convert scanned exercises
- Question-bank photos into an interactive practice application
- Including typed answer controls
- Persistent attempts
Example prompts
- “/scan-to-practice”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Source fidelity over AI invention. Questions and official answers must come from authorized source material. Clearly label generated…
- Fix data before presentation. Correct transcription and structural problems in source data after making a backup. Keep rendering logic…
- Validate the complete dataset. Sampling is useful for progress reports, not for final conclusions. A script finishing successfully does…
- Confirm consequential decisions. Ask the user to approve transcription scope, answer format, information architecture, and directory…
- Clarify visual or animation changes. Users may strongly value distinctive interactions; confirm the intended behavior before replacing or…
What it can do on your machine
Read from SKILL.md and the folder at commit cf8511e. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Scan To Practice loads about 2k tokens when it runs. Until then it costs about 128 tokens; SKILL.md has 942 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from parz0val0/scan-to-practice at commit cf8511e, republished under its MIT licence (© parz0val0). 942 words, ~2,004 tokens.
Download SKILL.mdSave it as
.claude/skills/scan-to-practice/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.- name
- scan-to-practice
- description
- A complete methodology for turning scanned or image-based learning materials into high-quality desktop, web, or mobile practice products. Covers visual transcription, data assembly, answer-key-driven controls and grading, product design, animation, validation, and long-term maintenance. Use when a user wants to convert scanned exercises, workbook pages, or question-bank photos into an interactive practice application, including typed answer controls, persistent attempts, and mistake review.
Scan-to-Practice: Scanned Materials to Practice Product
When to use this skill
- The user has scanned PDFs, workbook pages, or question-bank photos and wants an interactive text-based practice product.
- The product needs daily plans, progressive difficulty, progress tracking, mistake review, and multilingual explanations.
- Verified answer records need to become in-place choice, true/false, numeral, or free-text practice controls.
- The user wants to evaluate feasibility, architecture, cost, or quality controls before implementation.
Core principles
- Source fidelity over AI invention. Questions and official answers must come from authorized source material. Clearly label generated examples as synthetic or unofficial.
- Fix data before presentation. Correct transcription and structural problems in source data after making a backup. Keep rendering logic focused on presentation.
- Validate the complete dataset. Sampling is useful for progress reports, not for final conclusions. A script finishing successfully does not prove content correctness.
- Confirm consequential decisions. Ask the user to approve transcription scope, answer format, information architecture, and directory structure before large-scale work.
- Clarify visual or animation changes. Users may strongly value distinctive interactions; confirm the intended behavior before replacing or removing them.
Nine-stage pipeline
1. Rights and source audit
2. Library design: structure, difficulty, and schedule
3. Visual transcription: image to text
4. Data assembly and validation
5. Self-contained content format
6. Application architecture
7. Visual system
8. Animation and assets
9. Packaging, verification, and maintenanceDetailed references:
docs/01-project-journey.md— the complete delivery journey and implementation sequencedocs/02-troubleshooting.md— failure modes organized as symptom, root cause, fix, and verificationdocs/03-methodology.md— the reusable pipeline, design system, cost model, and tool checklistdocs/04-answer-interaction.md— answer-key-driven controls, grading state, failure handling, and coverage verification; read this when implementing answer entry or grading
Quick decision card
| Question | Recommended approach |
|---|---|
| Does the PDF contain a text layer? | Test a representative page with PyMuPDF get_text(). An empty result usually means visual transcription is required. |
| Which transcription engine? | Benchmark a capable paid vision model on representative pages. Conventional OCR may fail on dense tables, answer lines, italics, and complex layouts. |
| What should the prompt require? | Complete transcription; preserve numbering, blank lines, and tables; output source text only; do not explain or translate. |
| How should cost be estimated? | Measure tokens, latency, and retry rates on a small sample, then extrapolate. A reference run of 2,584 pages used about 7.3M tokens, CNY 23–42, and 8–12 background hours. |
| What are the major risks? | Reasoning tokens consuming the output budget, two-dimensional layouts collapsing, long documents being truncated, and segmentation based on ordinary body words. |
| How should difficulty be assigned? | Combine domain consensus, published statistics, task cognitive load, and later calibration from user accuracy. |
| Which desktop stack? | Electron is practical for a rich local interface. Use either simple native JavaScript or a modern React-based stack according to team size. |
| How should answer controls be generated? | Parse verified answers by question number, classify the response type, extract options from the same question range, and attach controls only to matching rendered anchors. Report gaps instead of fabricating structure. |
| What visual direction worked? | A restrained paper-inspired theme, low-chroma OKLCH colors, consistent spacing, and deliberate easing such as cubic-bezier(0.16,1,0.3,1). |
| How should validation work? | Layer syntax checks, full-dataset smoke tests, source-consistency assertions, answer-control coverage from production functions, browser-level interaction tests, and screenshot review. |
| What follows a source-data change? | Synchronize every runtime copy, rebuild indexes, and rerun the full verification suite. |
Show full SKILL.md (432 more words)Show less
Validated implementation notes
- Visual transcription averaged roughly 10–13 seconds per page in the reference project.
- Disable unnecessary model reasoning when the provider supports it; otherwise reasoning may consume the output budget and return empty transcription.
- Write each completed page to disk immediately and support resumable processing.
- Use structure anchors such as
SECTION,READING PASSAGE N, andWRITING TASK N; never classify a page from ordinary body-text keywords. - Reconstruct maps and plans programmatically from measured row and column anchors rather than manually counting spaces.
- Subset large fonts with
pyftsubset, then verify every required character. - Use lossless image compression such as
oxipngfor distributable assets. - Extract the actual rendering functions for assertions so test logic cannot silently drift away from production behavior.
- Treat the rendered exercise as the visual source of truth and the verified answer key as grading semantics; never create a control for an answer record without a matching question anchor.
- Classify true/false, yes/no, letter, multiple-letter, Roman-numeral, and free-text answers before rendering controls.
- Key persisted attempts by practice ID, section, and question number, and update only the affected control and mistake summary after grading.
- Normalize free-text answers conservatively and test both accepted variants and near-miss negatives.
- Keep animation frames limited to
transformandopacitywhenever possible.
Common traps
- Markdown table column mismatches can drop cells or prevent table parsing; normalize columns before rendering.
- Long underscore sequences may be interpreted as emphasis; use escaped entities or CSS borders for answer lines.
- An unclosed
<details>element can swallow the rest of the document; assert matching opening and closing counts. - CSS
displayrules can override the HTMLhiddenattribute; add explicit[hidden]{display:none}rules where required. - Full-width spaces can break tables, while normal spaces may be essential for diagrams; normalize them separately.
- A global regular expression reused with
test()carrieslastIndexstate and can skip lines. - A
Questions N-Mrange heading is context, not an answerable row; broad number matching can attach controls to the wrong element. - Global option extraction can borrow labels from an unrelated question range, while broad fuzzy matching can mark a wrong free-text answer as correct.
- Batch success counts prove execution, not content quality; inspect boundaries, compare backups, and run content-level assertions.
- Validate the validators against known-good and known-bad fixtures.
Maintenance protocol
When a new failure pattern appears:
- Reproduce it on a concrete source fragment.
- Decide whether it belongs to data, rendering, interaction, or environment.
- Implement a general rule rather than a one-file patch.
- Measure the full-dataset impact.
- Run targeted checks and the full regression suite.
- Record the new pattern in
docs/02-troubleshooting.mdor the appropriate reference.
© parz0val0, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 11 other files in the repository root of parz0val0/scan-to-practice.
- SKILL.md
- .gitattributes
- .gitignore
- LICENSE
- README.md
- docs/01-project-journey.md
- docs/02-troubleshooting.md
- docs/03-methodology.md
- docs/04-answer-interaction.md
- docs/assets/app-home.png
- docs/assets/app-practice.png
- docs/assets/source-to-app.png
Open the folder on GitHubat commit cf8511e
Compare with similar skills
Scan To Practice next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Scan To Practice this skillparz0val0/scan-to-practice | 109 | — | ~2k | Automated safety check: Pass | MIT | |
| HyperFrames Media Useheygen-com/hyperframes | 60k | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| Native Subtitle Quote Imagechengyi-ai/native-subtitle-quote-image | 2.6k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Edu Chem Videowy51ai/edulab | 1.4k | — | ~2.1k | Automated safety check: Notes | Apache-2.0 | |
| Transcription Memory ReconstructionNxcoreAI/EverRoom | 3k | — | ~714 | Automated safety check: Pass | Custom licence | |
| Edu Math Videowy51ai/edulab | 1.4k | — | ~2.5k | Automated safety check: Notes | Apache-2.0 |
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Related
Categories
Questions about Scan To Practice
What does Scan To Practice do?
A complete methodology for turning scanned or image-based learning materials into high-quality desktop, web, or mobile practice products. Scan To Practice is an agent skill from parz0val0/scan-to-practice. A complete methodology for turning scanned or image-based learning materials into high-quality desktop, web, or mobile practice products.
When should I use Scan To Practice?
Scan To Practice fits situations like: A user wants to convert scanned exercises; question-bank photos into an interactive practice application; including typed answer controls; persistent attempts.
How do I install Scan To Practice in Claude Code?
Run `npx skills add parz0val0/scan-to-practice --skill scan-to-practice -a claude-code`. Or copy the skill folder (the parz0val0/scan-to-practice repository) into .claude/skills/scan-to-practice in your project. Claude Code loads it when a task matches its description.
How do I install Scan To Practice in Codex?
Run `npx skills add parz0val0/scan-to-practice --skill scan-to-practice -a codex`. Or copy the skill folder (the parz0val0/scan-to-practice repository) into .agents/skills/scan-to-practice in your project. Codex loads it when a task matches its description.
Can I use Scan To Practice in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add parz0val0/scan-to-practice --skill scan-to-practice -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scan-to-practice, .gemini/skills/scan-to-practice, .github/skills/scan-to-practice and .opencode/skills/scan-to-practice in your project.
What does Scan To Practice need to run?
SKILL.md names no scripts, command-line tools or credentials: Scan To Practice is instructions for the agent only.
Does Scan To Practice access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Scan To Practice safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Scan To Practice use?
Scan To Practice is published under the MIT licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Scan To Practice use?
About 2k tokens (SKILL.md is roughly 8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Scan To Practice?
Skills that share tags, products or a category with Scan To Practice: HyperFrames Media Use (heygen-com/hyperframes, 60k stars), Native Subtitle Quote Image (chengyi-ai/native-subtitle-quote-image, 2.6k stars), Edu Chem Video (wy51ai/edulab, 1.4k stars) and Transcription Memory Reconstruction (NxcoreAI/EverRoom, 3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Scan To Practice?
parz0val0 (a GitHub user) maintains it in parz0val0/scan-to-practice, which has 109 GitHub stars. The repository was last updated on August 12, 2026.
Source: parz0val0/scan-to-practice on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.