Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ericgandrade/claude-superskills --skill us-program-researchgit clone --depth 1 https://github.com/ericgandrade/claude-superskillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ericgandrade/claude-superskills/us-program-research)<a href="https://agentmods.dev/skills/ericgandrade/claude-superskills/us-program-research"><img src="https://agentmods.dev/badge/skills/ericgandrade/claude-superskills/us-program-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ericgandrade/claude-superskills/us-program-research"><img src="https://agentmods.dev/badge/skills/ericgandrade/claude-superskills/us-program-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00027 | $0.03009 |
| Opus 5 | $0.00014 | $0.01504 |
| Sonnet 5 | $0.00005 | $0.00602 |
| Haiku 4.5 | $0.00003 | $0.00301 |
Grade A, and why
us-program-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- PHASE 0 — detect program type (FIRST QUESTION)
- PHASE 0B — collect all inputs before researching
- PHASE 2 — launch 4 subagents in ONE message (true parallelism)
- PHASE 3 — launch 4 deep-research subagents in ONE message
- PHASE 4 — apply adaptive scorecard after all research is complete
- PHASE 5 — generate full document and save
- PHASE 6 — present inline report to user
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 268 lines · 27 tokens per session scan A 0987c6ee8e56
us-program-research is a skill published in the GitHub repository ericgandrade/claude-superskills (74 stars, last pushed 4mo ago), licensed MIT. It adds 27 tokens to every session and 3,009 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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