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.
/plugin marketplace add SteveGJones/ai-first-sdlc-practicesnpx agentmods add plugins/stevegjones/ai-first-sdlc-practices/sdlc-knowledge-basegit clone --depth 1 https://github.com/SteveGJones/ai-first-sdlc-practicesWrote 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/plugins/stevegjones/ai-first-sdlc-practices/sdlc-knowledge-base)<a href="https://agentmods.dev/plugins/stevegjones/ai-first-sdlc-practices/sdlc-knowledge-base"><img src="https://agentmods.dev/badge/plugins/stevegjones/ai-first-sdlc-practices/sdlc-knowledge-base.svg" alt="Measured on agentmods" height="20"></a>Grade A, and why
sdlc-knowledge-base 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 4d 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.
What it actually says
{
"name": "sdlc-knowledge-base",
"version": "0.3.0",
"description": "Filesystem-based project knowledge base — librarian agent, hash-tracked indexes, ingest/query/lint operations. Orthogonal to SDLC option choice.",
"author": { "name": "SteveGJones" },
"repository": "https://github.com/SteveGJones/ai-first-sdlc-practices",
"license": "MIT",
"keywords": ["sdlc", "knowledge-base", "research", "librarian", "rag", "evidence"]
}
What it installs
The manifest is a name and a version. 5 skills, 4 agents travel with it, and installing the plugin installs all of them — 665 tokens a session between them. Each is measured on its own page, and each can be installed alone.
- Skill kb-init A 66 tokens
- Skill kb-ingest-bulk A 112 tokens
- Skill kb-audit-query A 54 tokens
- Skill kb-ingest A 79 tokens
- Skill kb-ingest-batch A 63 tokens
- Agent research-librarian A 75 tokens
- Agent agent-knowledge-updater A 75 tokens
- Agent synthesis-librarian A 56 tokens
- Agent knowledge-extractor A 85 tokens
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.
- 4d ago First seen · 10 lines scan A 20ac7f214ff0
sdlc-knowledge-base is a plugin published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 25d ago), licensed MIT. Its token cost is not measured: this kind of file is read by the harness, not the model. 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 2026-08-30.
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