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 DolphinAI2026/dolphin-code --skill agentic-spec-to-knowledge-derivationgit clone --depth 1 https://github.com/DolphinAI2026/dolphin-codeWrote 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/dolphinai2026/dolphin-code/agentic-spec-to-knowledge-derivation)<a href="https://agentmods.dev/skills/dolphinai2026/dolphin-code/agentic-spec-to-knowledge-derivation"><img src="https://agentmods.dev/badge/skills/dolphinai2026/dolphin-code/agentic-spec-to-knowledge-derivation/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/dolphinai2026/dolphin-code/agentic-spec-to-knowledge-derivation"><img src="https://agentmods.dev/badge/skills/dolphinai2026/dolphin-code/agentic-spec-to-knowledge-derivation.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 | $0.00784 |
| Opus 5.5 | $0.00021 | $0.00314 |
| Sonnet 5.5 | $0.00011 | $0.00157 |
| Haiku 4.5 | $0.00005 | $0.00078 |
Grade A, and why
agentic-spec-to-knowledge-derivation 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 1mo 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.
The source is not reproduced here
Licensed GPL-3.0
The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
12 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.
- prompts/derive.md 710 B
- prompts/doctor.md 639 B
- prompts/repair.md 739 B
- rules/agentic-memory-alignment.md 804 B
- rules/derivation.md 4.5 KB
- rules/secret-redaction.md 751 B
- templates/acceptance-case.md 701 B
- templates/business-flow.md 1.5 KB
- templates/business-object.md 923 B
- templates/page-interaction.md 942 B
- templates/risk-constraint.md 700 B
- templates/system-blueprint.md 1.0 KB
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.
- 1mo ago First seen · 52 lines · 53 tokens per session scan A b73e1ecd0bf0
agentic-spec-to-knowledge-derivation is a skill published in the GitHub repository DolphinAI2026/dolphin-code (979 stars, last pushed 2mo ago), licensed GPL-3.0. It adds 53 tokens to every session and 784 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 2026-09-03.
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