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 agentmods add skills/chronoaiproject/ornn/text-summarizernpx skills add ChronoAIProject/Ornn --skill text-summarizergit clone --depth 1 https://github.com/ChronoAIProject/OrnnWrote 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/chronoaiproject/ornn/text-summarizer)<a href="https://agentmods.dev/skills/chronoaiproject/ornn/text-summarizer"><img src="https://agentmods.dev/badge/skills/chronoaiproject/ornn/text-summarizer.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00476 |
| Opus 5 | $0.00000 | $0.00238 |
| Sonnet 5 | $0.00000 | $0.00095 |
| Haiku 4.5 | $0.00000 | $0.00048 |
Grade A, and why
text-summarizer 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 6d 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
text-summarizer
A minimum-viable LLM skill — the smallest amount of code that takes structured input, calls an LLM, and emits structured output.
Contract
Input (stdin, JSON):
{ "text": "...long input...", "length": 60 }
text(string, required) — what to summarise.length(number, optional, default 60) — target word count for the summary.
Output (stdout, JSON):
{ "summary": "..." }
Errors — written to stderr as { "error": "...message..." } and exit code 1.
Required environment
| Var | Purpose |
|---|---|
ANTHROPIC_API_KEY |
Talks to Claude. Swap the SDK call to point at OpenAI / Gemini / your own backend; nothing else changes. |
Run locally
cd examples/text-summarizer
bun install
ANTHROPIC_API_KEY=sk-ant-... echo '{"text":"...","length":40}' | bun run src/index.ts
Adapt this
- Different model vendor — replace
Anthropicwith the SDK of your choice; the I/O shape stays. - Stream output — emit one JSON line per token instead of one final blob.
- Sanitise input — the current code passes
textto the model verbatim; for untrusted callers, strip control characters and cap length before the API call.
What ships with it
3 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.
- 6d ago First seen · 57 lines · 0 tokens per session scan A 4241a5a33adf
text-summarizer is a skill published in the GitHub repository ChronoAIProject/Ornn (20 stars, last pushed 6d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 476 tokens. 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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