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 NatsuFox/Tapestry --skill ingestgit clone --depth 1 https://github.com/NatsuFox/TapestryWrote 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/natsufox/tapestry/ingest)<a href="https://agentmods.dev/skills/natsufox/tapestry/ingest"><img src="https://agentmods.dev/badge/skills/natsufox/tapestry/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/natsufox/tapestry/ingest"><img src="https://agentmods.dev/badge/skills/natsufox/tapestry/ingest.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 | $0.00045 | $0.01314 |
| Opus 5 | $0.00023 | $0.00657 |
| Sonnet 5 | $0.00009 | $0.00263 |
| Haiku 4.5 | $0.00005 | $0.00131 |
Grade A, and why
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 9d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tapestry Ingest
When to use this skill
Use this skill when:
- A user shares URLs or links to web content
- You need to archive or ingest web content into the local knowledge base
- Raw source artifacts need to be normalized before feed-building or synthesis
- The user asks to "save", "archive", "ingest", or "capture" web content
- You need deterministic crawling and scraping before model-based analysis
Overview
Turn a URL into a repeatable deterministic three-step chain:
- capture the source
- normalize it into a feed entry
- store the resulting content in the local knowledge base
Use the bundled runner instead of hand-rolling fetch and parse steps in the conversation. This skill is the primitive acquisition layer: crawl the source, normalize the result, and persist durable artifacts. It does not perform model-based synthesis.
The runner auto-selects a crawler from the code-defined implementations under _src/crawlers/.
Workflow
- Collect every relevant URL from the current user request.
- Run the ingest runner. The script is at
ingest/_scripts/run.pyrelative to the tapestry skill root (i.e.,$skill_root/ingest/_scripts/run.py). Always run it from the tapestry skill root:
python ingest/_scripts/run.py \
"$ARGUMENTS"
- Pass
--textwhen the surrounding request text contains useful context worth preserving alongside the URLs. - Use
--list-crawlersif you need to inspect the currently available crawler ids. - Use
--crawler <id>only when the user explicitly wants to force a particular crawler instead of automatic matching. - Review the command output for the created feed, note, and handoff-ready artifacts.
- Synthesis behavior based on mode:
"auto": Agent evaluates note accumulation and decides whether to invoke$tapestry-synthesis. The decision should be based on:- Number of unmerged notes accumulated
- Content relevance and importance
- Whether immediate merge provides value vs. waiting for more content
- System load and performance considerations
"deterministic": Automatically invoke$tapestry-synthesisafter every successful ingest"manual": Only invoke$tapestry-synthesiswhen user explicitly requests it"batch": Wait until user requests batch synthesis of multiple ingests
- If the user wants a rigorous structured feed instead of the raw normalized artifact, route the next step through
$tapestry-feed. - Report back with the successful URLs, created paths, matched crawlers when available, and any failures.
What ships with it
1 file 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.
- 9d ago First seen · 123 lines · 45 tokens per session scan A ddbb06d46147
ingest is a skill published in the GitHub repository NatsuFox/Tapestry (64 stars, last pushed 4mo ago), licensed MIT. It adds 45 tokens to every session and 1,314 once invoked, about $0.0002 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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