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/datacore-one/datacorenpx agentmods add commands/datacore-one/datacore/archive-searchWrote 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/commands/datacore-one/datacore/archive-search)<a href="https://agentmods.dev/commands/datacore-one/datacore/archive-search"><img src="https://agentmods.dev/badge/commands/datacore-one/datacore/archive-search/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/commands/datacore-one/datacore/archive-search"><img src="https://agentmods.dev/badge/commands/datacore-one/datacore/archive-search.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 | $0.00011 | $0.01420 |
| Opus 5.5 | $0.00004 | $0.00568 |
| Sonnet 5.5 | $0.00002 | $0.00284 |
| Haiku 4.5 | $0.00001 | $0.00142 |
Grade A, and why
archive-search 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 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.
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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Command: /archive-search
Search archived content using semantic similarity via datacortex embeddings.
Command Context
When to Reference DIP-0017
Always reference when:
- Searching historical content
- Finding archived documents
- Retrieving content from server archives
Key decisions this DIP informs:
- Archive snapshots use datacortex embeddings
- Snapshots sync from server to local for search
- Full content fetched on-demand via SSH
Quick Reference
| Question | Answer |
|---|---|
| What gets searched? | Datacortex embedding snapshots |
| Where are snapshots? | [space]/.archive-snapshot/ (synced from server) |
| What's the embedding model? | sentence-transformers/all-mpnet-base-v2 |
| Can I get full content? | Yes, via --fetch option |
Integration Points
- DIP-0017 - Archive snapshot specification
- DIP-0004 - Datacortex embedding patterns
Usage
/archive-search <query> # Search all archives
/archive-search --space <name> <query> # Search specific space
/archive-search --reindex # Rebuild index (runs archive-indexer)
Examples
/archive-search "investor agreement terms"
/archive-search --space 1-teamspace "2019 financial statements"
/archive-search --fetch "1-tracks/legal/contracts/acme-2018.pdf"
/archive-search --sync # Sync snapshots from server first
Workflow
Phase 1: Snapshot Check
- Check local snapshots in
[space]/.archive-snapshot/ - If missing or stale, offer to sync from server:
Archive snapshots not found locally. Sync from server? [Y/n]
Phase 2: Semantic Search
- Embed query using datacortex model
- Search each space's snapshot:
- Load embeddings from
archive.db - Compute cosine similarity
- Rank by score
- Load embeddings from
- Return top results with excerpts
Phase 3: Content Retrieval (Optional)
If --fetch specified:
- SSH to server
- Cat file content
- Return to user
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 · 216 lines · 11 tokens per session scan A c136fe211aee
archive-search is a command published in the GitHub repository datacore-one/datacore (4 stars, last pushed yesterday), licensed MIT. It adds 11 tokens to every session and 1,420 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-09-03.
Other commands, from other repositories
voice-refresh
Refresh the founder-voice DNA from new Granola meetings (interactive, MCP-backed). Pulls meetings since the last refresh, harvests Assaf-only speech, runs the Stage 2-3 orchestrator, and presents a founder-gated delta. Never auto-writes voice-dna.md.
status
Show memex statistics and status including projects, memos, and pending items.
open
Open the memex vault in Finder or Obsidian.
plan-sdd
A command that creates an implementation plan from a written specification using Clean Architecture and TDD. TDD, or test-driven development, means writing tests before the code they check.
essay
AI reflection and essay delivery.
dig
Deep exploratory interview to discover unknowns and strengthen plans.