OfferClaw留学申请简历助手

Writes study abroad admissions CVs and resumes, primarily for master's applications. Covers education, research, internships, publications, and awards. Suppo...

Security Scan

The skill's code, files, and runtime instructions are consistent with a CV-writing helper that offers an optional local PDF export; nothing requests unrelated credentials or hidden network endpoints, though the PDF helper will create a local venv and install Python packages when used.

Purpose & Capability

The name/description (admissions CV writing + PDF export) match the included references and two export scripts. The Python exporter and CSS are appropriate for producing a PDF from Markdown; no unrelated services or credentials are requested.

Instruction Scope

SKILL.md is narrowly scoped to collecting CV data, validating it against local reference files, producing a review text, and optionally generating a tagged Markdown file for export. It does not instruct reading sensitive system files or exfiltrating data to remote endpoints. All referenced resources are local files included in the skill (references/* and scripts/*).

Install Mechanism

There is no platform-level install spec, but the included run.py creates a local virtualenv and runs pip install --requirement on first use. This will download Python packages (weasyprint, markdown) from PyPI and may pull native dependencies; this behavior is expected for a local PDF exporter but does require network access and may fail where system libraries for WeasyPrint are missing.

Credentials

The skill declares no environment variables, no credentials, and no config paths. The exporter reads local files (input .md, CSS, optional bundled fonts) only. No secrets are requested or required.

Persistence & Privilege

always is false and the skill does not modify other skills or global agent settings. The exporter will create a local venv (scripts/export-pdf/venv) and write output files (the PDF and the tagged .md) in user-specified or current directories; this is expected for a file-export feature.

Assessment

This skill appears to do what it says: collect CV info, draft English CV text, and optionally export a PDF locally. If you plan to use the PDF export, note that the exporter will create a virtual environment under scripts/export-pdf/venv and run pip to install weasyprint and markdown (this requires network access and may pull native libraries). Bundled fonts referenced by the script are not present in the file list — the script will fall back to local fonts unless you request bundled-only mode. If you prefer not to allow package installation or file writes, you can still use the drafting features without running the exporter, or run the export step in a controlled/sandboxed environment. If you want extra assurance, inspect or run the export scripts locally in a sandbox before granting file-system access to the agent.

Admissions CV / Resume Writing

Use When

The user wants to create, rewrite, polish, or export an English graduate admissions CV or resume from their education, research, internship, project, activity, award, and skills background.

Workflow

Use the user's language for all conversation, questions, explanations, and review notes, but keep the actual CV output in English unless the user explicitly asks otherwise.

Phase 1: Collect Information

Load ONLY references/info-requirements.md, then guide the user using that checklist and the material-mining prompts.

• Show the checklist so the user can provide everything at once if they prefer.
• After receiving input, identify what is already covered and ask follow-up questions only for missing required categories or experiences with thin detail.
• Collect by module in order: education -> experiences (priority: research -> internship -> project -> campus activity -> extracurricular activity) -> awards -> skills.
• Mine each experience for bullet-point material: what the user did, which methods/tools they used, and what outcomes followed.
• Use careful inference only as a questioning strategy. You may suggest likely details in question form, but you must keep only what the user confirms and discard what they reject or skip.
• Optional categories should be asked only when useful; they are not mandatory.

⛔ Do not proceed to Phase 2 until every required category in references/info-requirements.md has been addressed. A category is addressed only when the user has either provided all required sub-fields or explicitly confirmed they have nothing for that category. See the Validation Rules section in that reference.

Phase 2: Confirm Information and Judge Sufficiency

1. Summarize the collected information by section and ask the user to confirm accuracy and missing items.
2. Evaluate whether the material is sufficient using the criteria in references/info-requirements.md.
3. Tell the user clearly whether the material is sufficient or thin, and explain why.
4. If the material is thin, ask whether the user can add more detail such as actions, tools, methods, or measurable outcomes. If they cannot, explain that you can carefully infer draft bullet points for review.
   • If the user chooses to supplement, return to the relevant module in Phase 1 and then repeat Phase 2.
   • If the user chooses to skip supplementation, continue to Phase 3 Branch B.

⛔ Do not proceed to Phase 3 until the user explicitly confirms the summary and, when the material is thin, explicitly chooses whether to supplement or skip.

Phase 3: Produce the CV (Review Version)

Load references/writing-instructions.md and references/cv-format-example.md, then follow their drafting rules. The output at this phase is a clean review version without HTML tags, intended for human reading and confirmation.

Branch A: Sufficient Material

Generate the review version:

1. User-language summary — a concise section-by-section synopsis for quick fact-checking.
2. English full text — the complete CV content in plain text (no HTML div tags), following the structure and writing rules from the references.

Branch B: Thin Material

Step 1: Expand bullet points for review

Load references/bullet-expansion-guide.md and generate at least 3 bullet points per experience from the available material.

• Output each bullet in English plus a short explanation in the user's working language when that helps review.
• Clearly mark inferred content on the explanation line with [Inferred].
• Append a follow-up checklist of details that would still strengthen each experience.

⛔ Do not proceed to Step 2 until the user explicitly confirms or revises the expanded bullet points.

Step 2: Produce the review version

After user confirmation, generate the review version:

1. User-language summary — same as Branch A.
2. English full text — include user-approved content; omit rejected inferred content entirely; strip all markers ([Inferred], [Needs Detail]).
3. AI Notes — append after the English full text, listing remaining gaps and omitted content.

Phase 4: Quality Check and User Confirmation

Load references/quality-checklist.md and run the checklist against the review version (user-language summary + English full text).

• Group results by severity: Error / Warning / Pass.
• If there are Error-level issues, fix them and present the revised review version.
• After all errors are resolved, ask the user to confirm the final content.

⛔ Do not proceed to Phase 5 until the user explicitly confirms the review version.

Phase 5: Export

After user confirmation, ask whether the user wants a PDF. If yes:

1. Generate the tagged English Markdown from the confirmed English full text:
   • Add HTML div layout tags following the patterns in references/cv-format-example.md.
   • The tagged version is for rendering only — it must not alter, add, or remove any content from the confirmed English text.
2. Save the tagged Markdown as a .md file at the user-specified path. If none is given, save to the current working directory with the filename CV_<Full-Name>_<YYYYMMDD>.md (e.g., CV_Zhang_Yuhan_20260403.md).
3. Consistency check: compare the saved tagged Markdown (with tags stripped) against the confirmed English full text.
   • This is a content-level comparison; minor differences in symbols or formatting are acceptable.
   • If a content discrepancy is found, fix the file and re-check.
4. Export to PDF (automatically initializes the environment on first run):

   python3 scripts/export-pdf/run.py <input.md> <output.pdf>
   

   • Default font mode is auto: prefer bundled fonts when the platform allows packaged font files, otherwise fall back to similar local system fonts.
   • If the platform forbids packaged font files, use --font-source local-only to force local-font rendering.
   • If bundled fonts are required for brand consistency, use --font-source bundled-only so the export fails fast when font assets are unavailable.

All script paths are relative to the admissions-cv-writing/ skill directory.

Prerequisites: PDF export requires Python 3 and an internet connection (for first-time dependency installation). The Python entrypoint creates an isolated venv and installs weasyprint and markdown automatically. No global packages are modified. If bundled fonts are unavailable, PDF export still works by falling back to locally installed fonts with a similar style, but line breaks and spacing may vary slightly across platforms.

Output Format

• Review version (shown to user): user-language summary + clean English full text, no HTML tags.
• Tagged Markdown (written to file): English full text with HTML div layout tags, for PDF rendering only. Never shown directly to the user.
• Branch B review: additionally append an AI Notes section after the English full text.
• Quality check: group results as Error / Warning / Pass.