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Us Program Research

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ericgandrade
us-program-research

This skill should be used when the user needs structured research and ranking of US academic programs (PhD, Master

Overview

Publisherericgandrade
Repositoryclaude-superskills
Skill nameus-program-research
Stars
86
Forks
16
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ericgandrade on GitHub. Read the source before you install it.

Installation

Install the Us Program Research AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ericgandrade/claude-superskills/tree/main/skills/us-program-research
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ericgandrade/claude-superskills.git /tmp/claude-superskills
mkdir -p .claude/skills
cp -r /tmp/claude-superskills/skills/us-program-research .claude/skills/us-program-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Us Program Research in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Us Program Research on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Us Program Research is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

US Academic Program Research — Complete Workflow

Purpose

Run structured research on US academic programs with credential analysis, parallel discovery, adaptive scorecards, and generation of an actionable application plan in a deliverable format.

When to Use

Use this skill when the task requires:

  • Selecting and ranking programs (PhD, Master's, or Bachelor's) in the US
  • Detailed comparison of curriculum, costs, and admission requirements
  • Application strategy based on profile, budget, and timeline
  • A final consolidated document for decision-making and execution

Progress Tracking

Display progress before each research phase:

[███░░░░░░░░░░░░░░░░░] 15% — Phase 0: Program Type Detection & Input Collection
[██████░░░░░░░░░░░░░░] 30% — Phase 1: Profile & Credential Analysis
[█████████░░░░░░░░░░░] 45% — Phase 2: Parallel Discovery (4 subagents)
[████████████░░░░░░░░] 60% — Phase 3: Parallel Deep Research (4 subagents)
[███████████████░░░░░] 75% — Phase 4: Adaptive Scorecards & Ranking
[█████████████████░░░] 85% — Phase 5: Document Generation
[████████████████████] 100% — Phase 6: Inline Report & Recommendations

Workflow

Follow the phases defined below in sequence, maintaining source traceability and separating facts from inferences.

Personalized research on US academic programs (PhD, Master's MS/MBA/MPS, or Bachelor's). Analyzes the candidate's profile, runs parallel searches via subagents, identifies hidden gems, applies an adaptive scorecard, and generates a complete ACTION_PLAN.md with rankings, curricula, costs, and a step-by-step checklist.

Output language: Portuguese (matches user research context). Queries/subagents: English (required for search quality).


Execution Instructions

  1. PHASE 0 — detect program type (FIRST QUESTION)
  2. PHASE 0B — collect all inputs before researching
  3. PHASE 2 — launch 4 subagents in ONE message (true parallelism)
  4. PHASE 3 — launch 4 deep-research subagents in ONE message
  5. PHASE 4 — apply adaptive scorecard after all research is complete
  6. PHASE 5 — generate full document and save
  7. PHASE 6 — present inline report to user

CRITICAL: All search queries and subagent prompts MUST be in English.


PHASE 0 — Program Type Detection

Use AskUserQuestion with: "What type of US academic program are you looking for?"

Options:

  1. PhD / Doctorate — 4–7 years of research. Usually fully-funded (stipend + tuition).
  2. Master's — MS, MBA, MPS, MEng (1.5–3 years). Executive, technical, online or in-person.
  3. Bachelor's / Undergraduate — First or second degree (BS, BA). 2–4 years.

The entire workflow adapts to this answer.


PHASE 0B — Input Collection (2 rounds of AskUserQuestion)

Round A — Personal profile (all types, max 4 questions):

  1. Full name + email
  2. Country of origin + language(s) of diplomas
  3. Highest current degree: institution, diploma type, GPA/grade (e.g., "8.1/10")
  4. Desired field of study (e.g., Computer Science, Business Analytics, AI, Finance...)

Round B — Logistics and preferences (max 4 questions):

  1. Target city/state in the US (or "open to any location")
  2. Preferred format: in-person / 100% online / hybrid / no preference
  3. Total budget: under $20k / $20–50k / $50–80k / over $80k / seeking funding
  4. Desired start date: 2026 / 2027 / 2028 / flexible

Additional type-specific question:

  • PhD: Specific research area? Known advisors? Funded programs only?
  • Master's: Current role/level (Junior/Manager/Director/VP/C-Level)? Will you work during the program?
  • Bachelor's: First or second degree? Transferable credits? Large/small campus preference?

Optional inputs (offer, do not require): CV, academic transcripts, prior executive courses, US immigration status (Green Card / F1-OPT / H1-B / other).


PHASE 1 — Profile & Credential Analysis

Determine foreign degree equivalency and evaluation strategy BEFORE scoring any program.

Action: Consult references/credential-analysis.md for:

  • Degree equivalency table by country (includes risk for Brazilian Tecnólogo degree)
  • WES vs ECE comparison by program type
  • WES ICAP step-by-step logistics (8 steps + 2 scenarios: favorable vs unfavorable result)
  • Anti-downgrading analysis for VP/C-Level candidates (Master's only)
  • Advisor fit analysis for PhD + contact email template
  • In-state tuition logic (Green Card + 12-month domicile = eligible)

Brazilian Tecnólogo = HIGH RISK. WES may evaluate as Associate's instead of Bachelor's. Recommend institutional letter + ECE as second opinion if WES result is unfavorable.


PHASE 2 — Parallel Discovery (4 Subagents)

CRITICAL: Launch all 4 subagents in ONE SINGLE message (Task tool × 4 simultaneous).

Divide research among 4 subagents with subagent_type="general-purpose":

SubagentFocusMinimum Programs
ARegional / Local In-Person4
BNational Online / Brand Equity5
CHidden Gems (low cost)3
DReference RankingsTop 20 context

Action: Consult references/subagent-prompts.md for the full prompts (in English) for each subagent, including all fields to collect and search queries.

After all 4 subagents complete:

  • Deduplicate programs (same school may appear in multiple subagents)
  • Verify accreditation: AACSB (business), ABET (engineering) — absence penalizes Brand Equity
  • For PhD: verify if fully-funded vs self-funded
  • Classify into groups: 1 (primary in-person), 1.5 (online brand equity), 2 (technical/deprioritized)
  • Target: 12–20 unique programs for Phase 3

PHASE 3 — Parallel Deep Research (4 Subagents)

CRITICAL: Split the 12–20 programs into 4 groups (3–5 each) and launch in ONE single message.

For each program, collect: full curriculum, verified cost on official website, student reviews (Niche/Reddit/GMAT Club/GradCafe), admission requirements (GMAT/TOEFL/letters/deadline), alumni network.

Action: Consult references/subagent-prompts.md for the full deep research template (section "Deep Research Template") with all queries in English.


PHASE 4 — Adaptive Scorecards

Action: Consult references/scorecards.md for the complete scorecards.

Scorecard Selection by Program Type:

TypeScorecardKey Criteria
Master'sScorecard ABrand + Exec Readiness + Flexibility + Network + ROI + Satisfaction
PhDScorecard BResearch Reputation + Advisor Fit + Funding + Placement + Satisfaction
Bachelor'sScorecard CBrand + Career Launch + ROI + Campus Life + Satisfaction

Master's — Adaptive Weights by Candidate Level:

CriterionEXECUTIVESENIORSTANDARDCAREER_LAUNCHOPT_CRITICAL
Brand Equity2523232322
Executive Readiness*2522181020
Flexibility1616141216
Network Quality1515151512
ROI / Cost-Benefit1214162014
Student Satisfaction1010101010
STEM Designation00006

*For VP/C-Level = "Executive Readiness". For non-executives = "Career Launch Potential".

Tier Classification:

TierScoreLabelAction
Top 5≥ 80Highest PriorityMUST APPLY
Tier 270–79If Budget AllowsIF BUDGET ALLOWS
Tier 355–69BackupBACKUP ONLY
Tier 4< 55AvoidAVOID

💎 Hidden Gem = Score ≥ 70 AND Cost ≤ 50% of budget AND Satisfaction ≥ 8.0/10


PHASE 5 — Document Generation

Save as: {CANDIDATE_NAME}_US_PROGRAM_ACTION_PLAN.md (in current directory).

Action: Consult references/action-plan-template.md for the full document template in Portuguese, including all sections, ~30 aligned tables, and the Table Formatting Policy.

Required sections in the generated document: General Status → Priority Next Actions → Phase 1 (WES/ECE) → Phase 2 (Tests) → Phase 3 (Contact) → Phase 4 (Submission) → Evaluators → Selected Programs by Tier → Adaptive Scorecard + Full Ranking → Curriculum Comparison → Detailed Curricula → Cost Comparison → Admission Comparison → Why Each Program → Document Inventory → Notes.


PHASE 6 — Inline Report

Present to the user in chat (in Portuguese) after saving the file:

Block 1 — Candidate Profile (max 5 lines): type, field, role/education, estimated GPA, immigration status.

Block 2 — Top 5 Recommendations (compact table): | Rank | Program | Score | Cost | Highlight |

Block 3 — 💎 Hidden Gems (if identified): score ≥ 70 + low cost + satisfaction ≥ 8.0.

Block 4 — ⚠️ Alerts: programs without reviews (red flag), daytime-only for executives, advisors without recent publications (PhD), borderline degree → WES urgent.

Block 5 — URGENT Next Action: "What to do TODAY" — usually: start WES/ECE or contact advisor.

Block 6 — Generated File: ✅ Document saved: {NAME}_US_PROGRAM_ACTION_PLAN.md ({N} lines, {N} tables)


Additional Resources

Reference Files

Consult as needed during execution:

  • references/credential-analysis.md — Degree equivalencies, full WES/ECE logistics, anti-downgrading, advisor fit (PhD), in-state tuition logic
  • references/scorecards.md — Complete Scorecards A/B/C with detailed rubrics, ROI formula with numeric example, tier thresholds, and hidden gem definition
  • references/subagent-prompts.md — Complete prompts (in English) for the 8 subagents: 4 discovery (Phases 2A/B/C/D) and deep research template (Phase 3)
  • references/action-plan-template.md — Complete ACTION_PLAN.md template in Portuguese with all sections and aligned tables
  • references/research-sources.md — Mandatory research sources and universal red flags by program type

Critical Rules

  • All research queries and subagent prompts must be in English.
  • Do not skip credential analysis before scoring/ranking.
  • Do not present a final recommendation without citing sources and criteria.
  • Clearly differentiate confirmed data, assumptions, and recommendations.

Error Handling

ErrorLikely CauseAction
Program URL unavailable (404)University program page moved or removedNote inaccessibility; use cached data or alternative sources for that program
No funding data foundProgram doesn't publish funding info publiclyMark as "funding data unavailable"; recommend user contact program directly
Insufficient programs match criteriaFilters too restrictive (budget, location, ranking)Relax constraints and explain tradeoffs to user
Budget eliminates all optionsTarget budget below typical program tuitionInform user of realistic cost ranges; suggest funded programs or assistantships
Credential gap detectedUser credentials unlikely to meet minimum requirementsFlag disqualifiers; recommend strengthening credentials before applying
WebFetch blocked by university siteBot-blocking or requires loginUse alternate sources (US News, Grad Cafe, QS rankings) for that program's data

Example Usage

  1. "Rank MS in Data Science programs in the US with a budget of USD 50k."
  2. "Compare funded PhD options in Computer Science focused on distributed systems."
  3. "Build an application plan for an MBA starting in 2027."

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Us Program Research AI skill do?

This skill should be used when the user needs structured research and ranking of US academic programs (PhD, Master

Why use Us Program Research on TypingMind?

Because you install it once and use it with any model. Us Program Research is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Us Program Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ericgandrade/claude-superskills/tree/main/skills/us-program-research. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Us Program Research?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Us Program Research?

As many as you like. As long as a model supports skills, you can use Us Program Research with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Us Program Research AI skill free?

Yes. It is published on GitHub by ericgandrade under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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