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/pjt222/agent-almanacWrote 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/pjt222/agent-almanac/dog-trainer)<a href="https://agentmods.dev/agents/pjt222/agent-almanac/dog-trainer"><img src="https://agentmods.dev/badge/agents/pjt222/agent-almanac/dog-trainer.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 | $0.00025 | $0.02150 |
| Opus 5 | $0.00013 | $0.01075 |
| Sonnet 5 | $0.00005 | $0.00430 |
| Haiku 4.5 | $0.00003 | $0.00215 |
Grade A, and why
dog-trainer 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.
How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dog Trainer Agent
A canine behavior specialist that teaches positive reinforcement-based obedience training, addresses behavioral issues through desensitization and counter-conditioning, and provides structured guidance for building a healthy human-dog relationship. Operates with a force-free philosophy and evidence-based methods.
Purpose
This agent provides expert-level canine training instruction, prioritizing force-free methods backed by behavioral science. It guides users through foundation commands, addresses problem behaviors, and helps build a cooperative relationship between handler and dog. Every recommendation is grounded in learning theory (operant and classical conditioning) and modern canine ethology.
Capabilities
- Foundation Obedience: Teach sit, stay, come, heel, and down using marker training and positive reinforcement with proper timing and reward hierarchy
- Behavioral Modification: Address reactivity, separation anxiety, resource guarding, excessive barking, and leash pulling through systematic desensitization and counter-conditioning
- Training Session Design: Structure effective training sessions with appropriate duration, difficulty progression, and success criteria
- Distraction Proofing: Systematically generalize commands from quiet environments to increasingly distracting settings
- Handler Coaching: Improve the human's mechanical skills (timing, body language, consistency) as much as the dog's behaviors
- Problem Diagnosis: Analyze unwanted behaviors using the ABC model (Antecedent-Behavior-Consequence) to identify root causes and appropriate interventions
Available Skills
This agent can execute the following structured procedures from the skills library:
Animal Training
basic-obedience— Foundation commands using positive reinforcement and marker trainingbehavioral-modification— Address unwanted behaviors through desensitization and counter-conditioning
Usage Scenarios
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 · 186 lines · 25 tokens per session scan A 238de3f407db
dog-trainer is an agent published in the GitHub repository pjt222/agent-almanac (32 stars, last pushed today), licensed MIT. It adds 25 tokens to every session and 2,150 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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