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 SteveGJones/ai-first-sdlc-practices --skill kb-ingestgit 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/skills/stevegjones/ai-first-sdlc-practices/kb-ingest)<a href="https://agentmods.dev/skills/stevegjones/ai-first-sdlc-practices/kb-ingest"><img src="https://agentmods.dev/badge/skills/stevegjones/ai-first-sdlc-practices/kb-ingest/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/stevegjones/ai-first-sdlc-practices/kb-ingest"><img src="https://agentmods.dev/badge/skills/stevegjones/ai-first-sdlc-practices/kb-ingest.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 | $0.00079 | $0.01000 |
| Opus 5 | $0.00039 | $0.00500 |
| Sonnet 5 | $0.00016 | $0.00200 |
| Haiku 4.5 | $0.00008 | $0.00100 |
Grade A, and why
kb-ingest 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 11d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Base Ingest
Take a new source and integrate it into the project's knowledge base. This is the ingest operation in the three-operations model (ingest / query / lint).
Argument
A source. Can be:
- A local file path:
library/raw/some-paper.md,~/Downloads/dora-2024.pdf - A URL:
https://example.com/research-report - Pasted text:
<long pasted excerpt>
Preflight
- Verify the project has a knowledge base configured (the
[Knowledge Base]section inCLAUDE.mdexists). If not, run/sdlc-knowledge-base:kb-initfirst. - Verify the
agent-knowledge-updateragent is available (sdlc-knowledge-base plugin is installed).
Steps
1. Resolve the source
If the argument is a file path: verify the file exists and is readable. If the argument is a URL: prepare for WebFetch (the agent will fetch). If the argument is pasted text: capture it for the agent.
If the source is a URL or a path outside library/raw/, recommend (but don't require) saving a local copy to library/raw/ for provenance:
Source is at <url>. Recommended: save a local copy to library/raw/<descriptive-name>.md before ingesting so provenance is preserved. Continue with direct fetch? (y/N)
If the user accepts, proceed with WebFetch in the agent. If they want to save locally first, pause and let them.
2. Invoke the agent-knowledge-updater
Dispatch the agent-knowledge-updater agent with the source as input. The agent's workflow:
- Read the source
- Classify it (does it belong in the knowledge base?)
- Read the shelf-index to find existing files this source touches
- Make surgical updates or create new files
- Rebuild the shelf-index (incremental)
- Append to log.md
The updater is opinionated about what belongs in the knowledge base. If it determines the source belongs elsewhere (operational knowledge → CONTRIBUTING.md, ADRs, project tracker, auto-memory), it will say so and not ingest. Respect that decision.
3. Report the result
Print the agent's summary:
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.
- 11d ago First seen · 104 lines · 79 tokens per session scan A 04d38bae0b35
kb-ingest is a skill published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 1,000 once invoked, about $0.0004 per session on Opus 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 2026-08-30.
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