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/JKHeadley/instarnpx agentmods add skills/jkheadley/instar/knowledge-baseWrote 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/jkheadley/instar/knowledge-base)<a href="https://agentmods.dev/skills/jkheadley/instar/knowledge-base"><img src="https://agentmods.dev/badge/skills/jkheadley/instar/knowledge-base/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/jkheadley/instar/knowledge-base"><img src="https://agentmods.dev/badge/skills/jkheadley/instar/knowledge-base.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 51 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00000 | $0.01336 |
| Opus 5.5 | $0.00000 | $0.00534 |
| Sonnet 5.5 | $0.00000 | $0.00267 |
| Haiku 4.5 | $0.00000 | $0.00134 |
Grade A, and why
knowledge-base scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST http://localhost:${INSTAR_PORT:-4040}/knowledge/ingest \ How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
knowledge-base -- Searchable Knowledge Base for Instar Agents
Build a searchable knowledge base from external sources -- URLs, documents, transcripts, PDFs. Uses the existing MemoryIndex (FTS5) for search, so no new dependencies.
How It Works
The knowledge base is a set of markdown files in .instar/knowledge/ that MemoryIndex indexes alongside your other memory files. Each file has YAML frontmatter for metadata and is tracked in a catalog for browsing.
.instar/knowledge/
catalog.json # Registry of all ingested sources
articles/ # Ingested web articles
transcripts/ # Video/audio transcripts
docs/ # Curated reference documentation
Ingesting Content
Via CLI
# Ingest text content directly
instar knowledge ingest "Article content here..." --title "My Article" --tags "AI,agents"
# Ingest from a URL (fetch first, then ingest)
# Step 1: Fetch the content
python3 .claude/scripts/smart-fetch.py "https://example.com/article" --auto > /tmp/fetched.md
# Step 2: Ingest it
instar knowledge ingest "$(cat /tmp/fetched.md)" --title "Article Title" --url "https://example.com/article" --tags "topic1,topic2"
Via API
curl -X POST http://localhost:${INSTAR_PORT:-4040}/knowledge/ingest \
-H "Content-Type: application/json" \
-d '{
"content": "The article content...",
"title": "Article Title",
"url": "https://example.com/article",
"type": "article",
"tags": ["AI", "infrastructure"],
"summary": "Brief description"
}'
Via Agent Workflow
When the agent wants to ingest content during a session:
- Fetch the content (WebFetch, smart-fetch, transcript tools, or Read for local files)
- Clean it (strip navigation, ads, boilerplate)
- Call the ingest API or write the file manually:
# Write the markdown file with frontmatter
cat > .instar/knowledge/articles/2026-02-25-my-article.md << 'EOF'
---
title: "My Article"
source: "https://example.com/article"
ingested: "2026-02-25"
tags: ["AI", "infrastructure"]
---
# My Article
[Cleaned article content here]
EOF
# Sync the index to pick up the new file
instar memory sync
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 · 190 lines · 0 tokens per session scan A 3b3ec59d32b1
knowledge-base is a skill published in the GitHub repository JKHeadley/instar (84 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,336 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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