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 researchgit 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/research)<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/research"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/research/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/research"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/research.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 | $0.00023 | $0.01024 |
| Opus 5.5 | $0.00009 | $0.00410 |
| Sonnet 5.5 | $0.00005 | $0.00205 |
| Haiku 4.5 | $0.00002 | $0.00102 |
Grade A, and why
research 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 — 79 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 / research
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Polluting main branch with exploratory throwaway code or failing to distill findings into actionable dossiers.
- Observability: Traceability via
execution/parity_guard.py([SKILL])
Isolated Research Subagent Protocol
Purpose: Execute technical investigations, external API audits, and library benchmarks on isolated throwaway branches or within
.tmp/to guarantee zero dirty state in main working trees.
Workflow Bindings: Integrated into/brainstorm,/plan, and/github_scout.
🎯 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/research_dossier_templates.md |
Standard dossier schema, comparison matrix layout, and gotcha logs | Formatting findings in .tmp/research-<topic>.md |
scripts/research_guard.py |
Automated research dossier linter checking required sections and actionable recommendations | Validating research summaries before presentation |
🔬 1. The 4-Phase Research Protocol
[1. BRANCH/TMP] ───> [2. INVESTIGATE] ───> [3. DOSSIER] ───> [4. RESTORE]
Create throwaway Query APIs, read Write summary into Return to main
branch or .tmp/ docs, test spikes .tmp/research-*.md branch & cleanup
- Create Throwaway Isolation: When testing heavy code, create a scratch branch:
git checkout -b research/<topic>. For documentation/API queries, work directly inside.tmp/. - Execute Research Subagent: Spawn dedicated research subagent to query documentation, test API payloads, or benchmark algorithms.
- Capture Findings: Formulate a structured dossier in
.tmp/research-<topic>.mdcontaining Executive Summary, Comparison Matrix, and Actionable Next Steps. - Clean Up Working State: Return to original branch (
git checkout -) and prune throwaway branches (git branch -D research/<topic>).
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 · +48 lines 7a66c883708d
- 1mo ago First seen · 31 lines · 23 tokens per session scan A 23380eaa4293
research is a skill published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 1,024 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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