SuperAPI AI Directory

resume

Office & productivity

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Updated 10-15 09:48

About this skill

Shared Resume Workflow

This is the shared, agent-neutral definition of the resume workflow.

Use it when you need to:

  • improve an existing resume draft;
  • turn project notes or raw material into resume content;
  • select a template before intake;
  • run structured guided intake for missing fields;
  • keep the workflow reusable across Claude and non-Claude agents.

Core rule

This installed skill bundle is self-contained.

When the skill is installed correctly, this directory should contain:

  • template_catalog_cli.py
  • template_store_cli.py
  • agent_intake_cli.py
  • host_cli.py
  • render_cli.py
  • resume_runtime/
  • resume_core/examples/

Treat the bundled resume_runtime/ under this skill directory as the primary shared interface.

Use these public entrypoints:

  • template_catalog_cli.py — list built-in and stored templates and derive template_context
  • template_store_cli.py — save personal templates and promote candidate templates
  • agent_intake_cli.py — host-facing outer intake entrypoint
  • host_cli.py — lower-level structured session control
  • render_cli.py — render markdown/html/css output bundles from a manifest and profile

Equivalent bundled module paths also exist under:

  • resume_runtime/template_catalog_cli.py
  • resume_runtime/template_store_cli.py
  • resume_runtime/agent_intake_cli.py
  • resume_runtime/host_cli.py
  • resume_runtime/render_cli.py

Claude-specific wrappers under .claude/skills/resume/ are compatibility adapters, not the primary shared contract.

Recommended workflow

1. Choose the template first

Before parsing materials or asking for missing information, choose a template.

  • In the installed skill bundle, built-in template manifests live under resume_core/examples/templates/
  • Built-in template assets, including CSS, live under resume_core/examples/template-assets/
  • The current built-in templates are typora-classic and markdown-basic
  • load built-in and stored templates through template_catalog_cli.py
  • read asset_paths from the catalog CLI response when you need the concrete markdown/html/css file locations for a template
  • show template cards with template id, style, use cases, and required-content summary
  • let the user choose a built-in template, a stored template, upload a new template, or ask for a derivative template
  • once a template is chosen, use the returned manifest + derived checklist as the template_context for agent_intake_cli.py

If you know the absolute path of this installed skill directory, enumerate built-in templates with:

python3 /path/to/installed/skill/template_catalog_cli.py

If you are already in the installed skill directory, you can also run:

python3 template_catalog_cli.py

If you are developing inside this repository root, the bundled entrypoint is:

python3 skills/resume/template_catalog_cli.py

You can still override the defaults when needed:

python3 /path/to/installed/skill/template_catalog_cli.py \
  --examples-root /path/to/installed/skill/resume_core/examples \
  --generated-at 2026-04-16T12:00:00Z

If you need to inspect a built-in template directly, start from:

  • resume_core/examples/templates/typora-classic.v1.json
  • resume_core/examples/templates/markdown-basic.v1.json
  • resume_core/examples/template-assets/typora-classic/style.css
  • resume_core/examples/template-assets/markdown-basic/style.css

Do not assume the template files live beside skills/resume/SKILL.md. The skill file defines the workflow; the built-in template manifests and assets live in the repository paths above.

2. Choose the content path after template selection

Use one of these paths:

  • direct content entry
  • parsing existing materials
  • follow-up questioning only for missing template-required fields

If the work is straightforward rewrite/polish, stay in direct editing. If the work depends on structured intake state, missing-field projection, or follow-up rounds, route through the public intake/session CLIs.

3. Use the public intake flow for structured collection

For structured intake and follow-up:

  • call resume_runtime/agent_intake_cli.py as the public outer entrypoint
  • provide template_context when a template has already been chosen
  • let the runtime decide whether the turn should stay freeform, ask for material, parse material, continue a structured session, or hand off to drafting
  • only ask the current batch of questions materialized by the runtime
  • at the recommended-only boundary, collect only a yes/no continuation decision

4. Draft against the selected template

When enough information is available:

  • produce the resume draft that matches the selected template’s required structure
  • localize section headings, field labels, and fixed template copy to match the user’s language; Chinese requests should render Chinese labels by default
  • preserve evidence-based wording and truthful ownership
  • list any remaining missing facts explicitly instead of guessing

5. Render final template artifacts through the public renderer

When a profile is ready:

  • use resume_runtime/render_cli.py or the shared rendering helpers
  • generate markdown/html/css outputs from the selected manifest and profile
  • treat these rendered assets as the portable output bundle for host integrations and Typora-first preview flows

Host-neutral request shape

When calling resume_runtime/agent_intake_cli.py, use the public request envelope documented in README.md and this shared skill.

At minimum, expect these concepts:

  • version
  • turn
  • optional template_context
  • optional materials
  • optional drafting_started

Output expectations

A good run should usually produce:

  • a selected template or template decision
  • a filled or partially filled profile
  • a clear missing-information list when facts are still absent
  • a resume draft or rendered output bundle when enough information is present

Boundary rule

Shared workflow logic belongs in resume_runtime/ and shared docs like this file. Agent-specific prompt shaping belongs in that agent’s adapter layer.