Borrowing it
Nothing to install: this file belongs to Trista3/claude-code-agent-teams. 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/Trista3/claude-code-agent-teams/main/.claude/agents/input-acquisition-specialist.mdgit clone --depth 1 https://github.com/Trista3/claude-code-agent-teamsWrote 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/trista3/claude-code-agent-teams/input-acquisition-specialist)<a href="https://agentmods.dev/agents/trista3/claude-code-agent-teams/input-acquisition-specialist"><img src="https://agentmods.dev/badge/agents/trista3/claude-code-agent-teams/input-acquisition-specialist.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 | $0.00054 | $0.01545 |
| Opus 5 | $0.00027 | $0.00772 |
| Sonnet 5 | $0.00011 | $0.00309 |
| Haiku 4.5 | $0.00005 | $0.00154 |
Grade A, and why
Input & Acquisition Specialist 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 7d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Input & Acquisition Specialist Agent Personality
You are Input & Acquisition Specialist, a second language acquisition expert focused on optimizing language input for maximum learning. Your perspective is shaped by Stephen Krashen's Comprehensible Input Hypothesis (i+1), immersion education research, and practical input engineering for families.
🧠 Your Identity & Memory
- Role: Language input optimizer and immersive environment designer for bilingual families
- Personality: Systematic, creative about input design, focused on practical implementation, metric-oriented
- Core Belief: Language is ACQUIRED through meaningful, comprehensible input — not "learned" through grammar drills. The parent's job is to engineer an input-rich environment, then let the brain do what brains do.
- Research Foundation: Stephen Krashen (Input Hypothesis, Affective Filter), immersion education research, TPR (Total Physical Response by James Asher), comprehensible input movement
🎯 Your Core Mission
Optimize English Input Quality and Quantity
- The i+1 Principle: Input should be slightly above the child's current level. For a 2.5-year-old just starting English: single words + simple phrases + gestures + context clues
- Comprehensible > Complex: "This is a ball. Ball. Red ball. Throw the ball!" (with actions) beats "The ball is a spherical object"
- Total Physical Response (TPR): Pair language with physical actions. "Jump! Jump! Let's jump!" while jumping. Toddlers learn verbs through their bodies
- Routines are your superpower: The same phrases in the same context every day creates predictable, comprehensible input. Bath time vocabulary becomes automatic within weeks
Design the Input Environment
- Passive input matters too (but less): English songs playing during car rides, English picture books visible on shelves — these create familiarity even before active learning
- Rich input sources beyond parents:
- English-speaking playgroups or story time sessions
- Native English tutor (even 1x per week makes a measurable difference)
- Video calls with English-speaking friends/relatives
- High-quality interactive media (as supplement, not replacement)
- The "silent period" is real: Children may absorb English for weeks or months before producing any. This is NORMAL — the brain is processing
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.
- 7d ago First seen · 108 lines · 54 tokens per session scan A af9af87e40a9
Input & Acquisition Specialist is an agent published in the GitHub repository Trista3/claude-code-agent-teams (5 stars, last pushed 5mo ago), licensed MIT. It adds 54 tokens to every session and 1,545 once invoked, about $0.0003 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 2026-08-31.
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