Ingest
Local-first web scraping and source ingestion — fetch a URL, extract clean text locally (0 cloud tokens), optionally structure it with a local model, and store it in a Qdrant collection (the second-brain "foundation") for later recall. Use for /web-scraping, /ingest-source, building a knowledge foundation, or recalling past ingested sources. Web fetch + Qdrant over stdlib HTTP.
Install
$ agentstack add skill-zedarvates-botte-secrete-ingest ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ● Network access Used
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-zedarvates-botte-secrete-ingest) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
ingest — scrape the web & build a knowledge foundation, locally
Scraping and ingesting are extraction/transformation — keep them off the cloud.
python -m skills.ingest.cli scrape https://example.com --structure # local summary+entities
python -m skills.ingest.cli ingest https://example.com --collection foundation
python -m skills.ingest.cli ingest ./notes.md --file
python -m skills.ingest.cli search "topic" --collection foundation- scrape — fetch (browser UA) + stdlib HTML→text (drops script/style, keeps
title). --structure asks a LOCAL model for a 3-bullet summary + key entities. 0 cloud tokens.
- ingest — scrape (or a file/raw text) → reflect locally → upsert into Qdrant
(192.168.1.47:6333 by default). Builds the "foundation"/historical store.
- search — recall from a collection.
Embeddings: auto-resolve a local /v1/embeddings endpoint from the backend registry (any reachable backend exposing an embedding model → real semantic vectors), falling back to a deterministic hash n-gram (256-dim) when none is available — so it always works, 0 cloud tokens either way. Override with --embed-url/--embed-model. The result reports embed: endpoint|hash. Degrades gracefully when Qdrant is down (scrape still works).
python -m skills.ingest.cli ingest https://example.com --embed-url http://127.0.0.1:1234/v1/embeddingsExposed via [[llmmcp]] as scrape and ingest_source. Related: [[hermes-second-brain]] (the foundation concept), media_loader, [[autorouter]].
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: zedarvates
- Source: zedarvates/botte-secrete
- License: MIT
- Homepage: https://github.com/zedarvates/botte-secrete
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.