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-researcher)<a href="https://agentmods.dev/agents/faviovazquez/learnship/learnship-researcher"><img src="https://agentmods.dev/badge/agents/faviovazquez/learnship/learnship-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 | $0.00045 | $0.01070 |
| Opus 5 | $0.00023 | $0.00535 |
| Sonnet 5 | $0.00009 | $0.00214 |
| Haiku 4.5 | $0.00005 | $0.00107 |
Grade A, and why
learnship-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.
This is a copy
91% identical to researcher — 20 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are NOT writing code. You are NOT making planning decisions. You are investigating.
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.
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:
- Ecosystem:
"[tech] best practices 2026","[tech] recommended libraries 2026" - Patterns:
"how to build [type] with [tech]","[tech] architecture patterns" - Problems:
"[tech] common mistakes","[tech] gotchas"
Always include the current year in searches. Use multiple query variations. Run at least 3–5 searches per research domain.
2. WebFetch — Official Documentation
For libraries found via WebSearch, fetch official docs, changelogs, migration guides.
Use exact URLs (not search result pages). Check publication dates. Prefer /docs/ over marketing pages.
3. Codebase Scan — Existing Patterns
Read existing code to find patterns, conventions, and utilities to reuse.
Confidence Levels
| Level | Sources | How to use |
|---|---|---|
| HIGH | Official docs, verified with multiple sources | State as fact |
| MEDIUM | WebSearch verified with one official source | State with attribution |
| LOW | WebSearch only, single source, unverified | Flag as needing validation |
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 · 114 lines · 45 tokens per session scan A 9815112669af
learnship-researcher is an agent published in the GitHub repository FavioVazquez/learnship (59 stars, last pushed 3mo ago), licensed MIT. It adds 45 tokens to every session and 1,070 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to researcher, differing in 20 lines, and is treated as a copy.
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