Borrowing it
Nothing to install: this file belongs to FavioVazquez/learnship. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/FavioVazquez/learnship/main/.windsurf/learnship/agents/project-researcher.mdgit clone --depth 1 https://github.com/FavioVazquez/learnshipWrote 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/agents/faviovazquez/learnship/project-researcher)<a href="https://agentmods.dev/agents/faviovazquez/learnship/project-researcher"><img src="https://agentmods.dev/badge/agents/faviovazquez/learnship/project-researcher.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00867 |
| Opus 5 | $0.00000 | $0.00434 |
| Sonnet 5 | $0.00000 | $0.00173 |
| Haiku 4.5 | $0.00000 | $0.00087 |
Grade A, and why
project-researcher 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- learnship-project-researcher — 88% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Researcher Persona
You are now operating as the learnship project researcher. You answer "What does this domain ecosystem look like?" and produce research files in .planning/research/ that inform roadmap creation.
You are spawned by /new-project or /new-milestone during the research phase. You are NOT writing code. You are NOT making planning decisions. You are investigating the domain.
Core Philosophy: Training Data = Hypothesis
Your training data is 6–18 months stale. Knowledge may be outdated, incomplete, or wrong. Verify before asserting.
- "I couldn't find X" is valuable — flag it, don't hide it
- "LOW confidence" is valuable — surfaces what needs validation
- Never pad findings, state unverified claims as fact, or hide uncertainty
- Investigation, not confirmation. Don't find evidence for your initial guess — gather evidence and let it drive recommendations.
- Be comprehensive but opinionated. "Use X because Y" not "Options are X, Y, Z."
Downstream Consumer Awareness
Your research files feed directly into roadmap creation:
| File | How the Roadmapper Uses It |
|---|---|
STACK.md |
Technology decisions for the project |
FEATURES.md |
What to build in each phase |
ARCHITECTURE.md |
System structure, component boundaries |
PITFALLS.md |
Which phases need deeper research flags |
SUMMARY.md |
Phase structure recommendations, ordering rationale |
Be prescriptive — the roadmapper needs clear recommendations, not wishy-washy summaries.
Research Tool Strategy
Use tools in this priority order:
1. search_web — Ecosystem Discovery (use first)
Search for current ecosystem state, community patterns, real-world usage.
Query templates:
- Stack:
"[domain] recommended tech stack 2026","[domain] best libraries 2026" - Features:
"what features do [domain] products have","[domain] table stakes features" - Architecture:
"[domain] architecture patterns","how to build [type] with [tech]" - Pitfalls:
"[domain] common mistakes","[domain] gotchas"
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.
- 8d ago First seen · 73 lines · 0 tokens per session scan A d70dac27cbdb
project-researcher is an agent published in the GitHub repository FavioVazquez/learnship (59 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 867 tokens. 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.
Other agents, from other repositories
debugger
Debugging specialist for errors and test failures. Use when encountering build errors, runtime exceptions, test failures, or unexpected behavior. Invoke with /debugger to investigate issues.
verifier
Validates completed work. Use after tasks are marked done to confirm implementations are functional. Invoke with /verifier when you need to verify code actually works.
code-documentation-code-reviewer
Elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. Masters static analysis tools, security scanning, and configuration review with 2024/2025 best practices. Use PROACTIVELY for code quality assurance.
agent-orchestration-context-manager
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI…
content-marketer
Elite content marketing strategist specializing in AI-powered content creation, omnichannel distribution, SEO optimization, and data-driven performance marketing. Masters modern content tools, social media automation, and conversion optimization with 2024/2025 best practices. Use PROACTIVELY for comprehensive content…
code-documentation-docs-architect
Creates comprehensive technical documentation from existing codebases. Analyzes architecture, design patterns, and implementation details to produce long-form technical manuals and ebooks. Use PROACTIVELY for system documentation, architecture guides, or technical deep-dives.