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 parallel-agentsgit 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/parallel-agents)<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/parallel-agents"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/parallel-agents/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/parallel-agents"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/parallel-agents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00029 | $0.01055 |
| Opus 5.5 | $0.00012 | $0.00422 |
| Sonnet 5.5 | $0.00006 | $0.00211 |
| Haiku 4.5 | $0.00003 | $0.00105 |
Grade A, and why
parallel-agents 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 12d 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 — 83 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 / parallel-agents
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Cyclic worker dependencies, payload bloat across agent handoffs, or uncoordinated file clobbering.
- Observability: Traceability via
execution/parity_guard.py([SKILL])
Native Parallel Agent Orchestration
Purpose: Coordinate multiple specialized agents for multi-domain initiatives, comprehensive system audits, and fan-out/fan-in architectural workflows.
Workflow Bindings: Integrated directly into/orchestrateand/plan.
🎯 Progressive Disclosure & L3 Reference Index
Read the core Sovereign rules below; consult deep L3 Reference Guides on demand:
| L3 Reference Guide | Purpose | Trigger / When to Load |
|---|---|---|
references/orchestration_patterns.md |
Full 26-agent directory, multi-agent fan-out trees, context handoff schemas | Multi-agent coordination & domain routing |
scripts/orchestration_guard.py |
Automated briefing auditor and dependency cycle detector | Validating worker task packets and dependency topologies |
🧭 1. Orchestration Decision Flow
User Task Analysis:
├── Single Domain / Local Fix ➔ Direct single specialist (No orchestration overhead)
└── Multi-Domain Initiative ➔ Parallel Agent Fan-Out (Explore ➔ Implement ➔ Verify)
⚡ 2. Core Execution Topologies
- Comprehensive Analysis (Fan-Out/Fan-In): Read-only parallel exploration (
security-auditor+performance-optimizer) followed by a central synthesis turn. - Feature Implementation (Pipelined): Sequential execution where database migrations and Rust backend services precede React frontend UI wiring.
- Verification Fan-Out (Parallel Gates): Independent parallel validation (
test-engineerruns Vitest whilelint-runnervalidates types and invariants).
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.
- 12d ago Changed · +29 lines 9806fc091182
- 1mo ago First seen · 54 lines · 29 tokens per session scan A 7e448dee5f75
parallel-agents is a skill published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 1,055 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 2026-09-03.
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