Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/DDS-Solutions/AI-TadPole-OSnpx agentmods add skills/dds-solutions/ai-tadpole-os/skillifyWrote 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/skillify)<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/skillify"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/skillify/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/skillify"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/skillify.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 | $0.00037 | $0.01257 |
| Opus 5.5 | $0.00015 | $0.00503 |
| Sonnet 5.5 | $0.00007 | $0.00251 |
| Haiku 4.5 | $0.00004 | $0.00126 |
Grade A, and why
skillify 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 — 93 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 / skillify
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Creating non-compliant skill layouts, oversized instruction bodies (>5k words), or omitting deterministic scripts in
scripts/.- Observability: Traceability via
execution/parity_guard.py([SKILL])
Skillify — Auto-Create Sovereign Skills from Workflows
Philosophy: Turn repetitive patterns into reusable skills. If you have executed the same multi-step pattern 3+ times, codify it as an agentskills.io compliant skill package with L1 metadata, L2 instructions, and L3 references/scripts.
Workflow Bindings: Integrated into/codifyand/anneal.
🎯 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/skill_authoring_spec_and_templates.md |
Full agentskills.io Open Specification, L1/L2/L3 disclosure hierarchy, frontmatter schemas | Authoring new skills or auditing existing skills |
scripts/skill_scaffolder.py |
Automated skill package generator creating SKILL.md, references/, and scripts/ with tests |
Scaffolding new skills from the command line |
🧭 1. When to Skillify
✅ Good Candidates:
- The user or swarm has requested the same multi-step workflow 3+ times.
- The process requires 4+ consistent procedural steps with deterministic tools.
- The workflow applies across multiple features, components, or repos.
- Other agents benefit from shared domain wisdom and automated scripts.
❌ Bad Candidates:
- One-off bug fixes or ad-hoc queries (just do them directly).
- Ephemeral user preferences (store in
.agent/memory/user-preferences.md). - Overlapping logic already handled by an existing skill.
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 · -41 lines 91de78958a7a
- 1mo ago First seen · 134 lines · 37 tokens per session scan A c50cb15de303
skillify is a skill published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 1,257 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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