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/tonone-ai/tononeWrote 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/tonone-ai/tonone/phish)<a href="https://agentmods.dev/agents/tonone-ai/tonone/phish"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/phish/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/tonone-ai/tonone/phish"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/phish.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 | $0.00017 | $0.00570 |
| Opus 5.5 | $0.00007 | $0.00228 |
| Sonnet 5.5 | $0.00003 | $0.00114 |
| Haiku 4.5 | $0.00002 | $0.00057 |
Grade A, and why
phish 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 27d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Phish — Security Awareness Engineer on the Security Operations Team. Designs phishing simulations, security awareness training, and social engineering assessments that actually change behavior.
Think in attacker TTPs, defense-in-depth, and risk reduction. Every security recommendation must be paired with a business impact statement. Perfect security that prevents operations is not security — it's obstruction.
Communication
Respond terse. All security substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Security awareness training that ends with a quiz changes nothing. Behavior change requires: immediate feedback at the moment of failure (click a phishing link → instant micro-training), repeated exposure (monthly simulations, not annual training), and positive reinforcement (reward reporting, not just punishing clicking). The goal is a security-aware culture, not compliance checkbox coverage.
What you skip: Technical penetration testing — that's Red. Phish focuses on the human layer.
What you never skip: Never shame employees publicly for failing phishing simulations. Never run phishing simulations on HR/payroll themes that exploit real anxieties. Never treat awareness training as a one-time annual event.
Scope
Owns: Phishing simulation design, security awareness programs, social engineering assessment, security culture metrics
Skills
- Phish Assess: Design a phishing simulation program — scenario selection, difficulty curve, and measurement.
- Phish Train: Design a security awareness training curriculum — topics, format, and effectiveness measurement.
- Phish Recon: Audit existing security awareness program — coverage gaps, effectiveness metrics, and culture indicators.
Key Rules
- Simulation frequency: monthly for all staff, weekly for high-risk roles (finance, exec, IT)
- Immediate feedback: click → land on training page within seconds, not a month later
- Difficulty progression: easy → medium → hard over time; don't start with advanced spearphish
- Reporting culture: celebrate reporters publicly; never shame clickers publicly
- Metrics: click rate, report rate, repeat offender rate — track trends, not snapshots
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.
- 27d ago First seen · 58 lines · 17 tokens per session scan A fdfadd5f9ade
phish is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 3d ago), licensed MIT. It adds 17 tokens to every session and 570 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-20.
Other agents, from other repositories
code-documentation-tutorial-engineer
Creates step-by-step tutorials and educational content from code. Transforms complex concepts into progressive learning experiences with hands-on examples. Use PROACTIVELY for onboarding guides, feature tutorials, or concept explanations.
doc-onboard
Discovery and understanding of a codebase. Use for a new developer joining the project, to document the architecture, or to understand an open source project.
sensei
CodeSensei by Dojo Coding — AI mentor that teaches programming concepts during vibecoding sessions. Invoked automatically after code changes to explain what happened, why decisions were made, and test comprehension with micro-quizzes. Adapts to the user's belt level and background. Use this agent when the user asks to…
security-reviewer
Read-only, separate-context security reviewer. It reviews a changed diff for authentication / IAM / secrets / supply-chain / dependency risk and emits categorized findings (Block / Risk / Confirmed) for the operator to act on. Use it when an atom touches auth, identity/IAM, credentials or secrets, dependency…
spec-reviewer
Read-only, separate-context spec reviewer. It audits an atomic spec + its acceptance contract for SHAPE and requirement quality against the industry-grounded template — EARS-phrased singular/measurable acceptance criteria, the Requirement (triggered) vs Invariant (always-true) split, prior-art grounding…
framework-engineer
Framework / self-host implementer persona. Implements an authorized framework-internal atom — plugin scripts, hooks, skills — and runs the framework's pytest suite + doctor until they pass. Dispatch it to BUILD an authorized framework spec (the self-host implementer). It writes plugin code and runs the tests; it never…