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.
git 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/learnship-project-researcher)<a href="https://agentmods.dev/agents/faviovazquez/learnship/learnship-project-researcher"><img src="https://agentmods.dev/badge/agents/faviovazquez/learnship/learnship-project-researcher/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/agents/faviovazquez/learnship/learnship-project-researcher"><img src="https://agentmods.dev/badge/agents/faviovazquez/learnship/learnship-project-researcher.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 | $0.00069 | $0.00955 |
| Opus 5 | $0.00034 | $0.00477 |
| Sonnet 5 | $0.00014 | $0.00191 |
| Haiku 4.5 | $0.00007 | $0.00096 |
Grade A, and why
learnship-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 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.
This is a copy
88% identical to project-researcher — 22 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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. WebSearch — 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.
- 9d ago First seen · 79 lines · 69 tokens per session scan A 72e4a8265c75
learnship-project-researcher is an agent published in the GitHub repository FavioVazquez/learnship (59 stars, last pushed 3mo ago), licensed MIT. It adds 69 tokens to every session and 955 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to project-researcher, differing in 22 lines, and is treated as a copy.
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