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.
npx skills add DDS-Solutions/AI-TadPole-OS --skill behavioral-modesgit clone --depth 1 https://github.com/DDS-Solutions/AI-TadPole-OSWrote 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/skills/dds-solutions/ai-tadpole-os/behavioral-modes)<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/behavioral-modes"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/behavioral-modes/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/skills/dds-solutions/ai-tadpole-os/behavioral-modes"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/behavioral-modes.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 | $0.00053 | $0.02194 |
| Opus 5.5 | $0.00021 | $0.00878 |
| Sonnet 5.5 | $0.00011 | $0.00439 |
| Haiku 4.5 | $0.00005 | $0.00219 |
Grade A, and why
behavioral-modes 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 6d 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[!IMPORTANT] AI Context & Knowledge Heritage
- Subsystem: Agent Skills Registry / behavioral-modes
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Mode bleed, premature execution, ungrounded conversational drift.
- Observability: Traceability via
execution/parity_guard.py([SKILL])
🎭 Behavioral Modes - Adaptive AI Operating Modes
Behavioral modes eliminate "mode bleed"—the tendency for an AI agent to be overly conversational during implementation or rigid during early architectural brainstorming. Modes dictate internal operational logic, problem-solving posture, and external communication style.
📚 Progressive Disclosure & L3 Reference Index
For deep output schemas, state machine diagrams, and concrete Plan-Execute-Critic (PEC) handovers:
- State Machine Transitions & Concrete Examples: mode_transitions_and_examples.md
- Deterministic Validator: validate_behavioral_modes.py
⛓️ The Sovereign Pipeline Integration
Operational modes are not arbitrary personas; they represent discrete stages of the software development lifecycle:
BRAINSTORM (Socratic Gate) $\rightarrow$ ORCHESTRATE (Planning) $\rightarrow$ ALETHEIA (Reasoning) $\rightarrow$ IMPLEMENT (Coding) $\rightarrow$ REVIEW (Audit) $\rightarrow$ SHIP (Release)
Support modes (DEBUG and TEACH) branch dynamically based on execution feedback and user intent.
🛠️ The 8 Operational Modes
1. 🧠 BRAINSTORM Mode
When to use: Early planning, feature ideation, requirement discovery, ambiguous requests.
- Behavior: Divergent thinking. Explore multiple architectural approaches. Focus on the "What" and "Why."
- Constraint: Socratic Gate applies. The AI must surface trade-offs and ask targeted clarifying questions to narrow the solution space. No implementation code is written until alignment is confirmed.
- Output Style: Options-based. Format as structured options with pros, cons, and risks.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago Changed · +17 lines · +36 tokens per session bbc09b71e813
- 1mo ago Changed 1eb88e86b4fd
- 1mo ago First seen · 138 lines · 17 tokens per session scan A 0a1d7a2fb02d
behavioral-modes is a skill published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed 2d ago), licensed MIT. It adds 53 tokens to every session and 2,194 once invoked, about $0.0002 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 2026-08-31.
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