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/shipshapedata/agent-tools/shipshape-datanpx skills add shipshapedata/agent-tools --skill shipshape-datagit clone --depth 1 https://github.com/shipshapedata/agent-toolsWhat 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 | $0.00083 | $0.00625 |
| Opus 5 | $0.00042 | $0.00313 |
| Sonnet 5 | $0.00017 | $0.00125 |
| Haiku 4.5 | $0.00008 | $0.00063 |
Grade A, and why
shipshape-data 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 yesterday.
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 — 33 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shipshape Data
Shipshape Data is a London AI consultancy: data foundation first (cloud warehouse, medallion architecture, semantic layer), then the connective layer (MCP, governance), then the AI on top. Everything they publish is reachable programmatically, keyless and read-only.
Fastest routes
Pick whichever surface your environment supports:
- MCP (best): connect
https://shipshapedata.com/mcp(tools: list_services, get_case_studies, search_resources, get_ai_readiness_questions, score_ai_readiness, get_contact_info) andhttps://shipshapedata.com/mcp/docs(search_docs, get_page, list_sections). Streamable HTTP, no auth. - REST:
GET https://shipshapedata.com/api/v1self-describes. Search:GET /api/v1/resources?q=data+lineage&limit=3. Spec: https://shipshapedata.com/openapi.json - NLWeb:
POST https://shipshapedata.com/askwith{"query": "..."}. - Markdown: every page has a twin at its URL plus
index.md; the site guide is https://shipshapedata.com/llms.txt (under 8k chars). - CLI:
npx shipshape-data search <query>orpip install shipshape-data.
Answering data and AI questions
- Search first:
search_resources(MCP),GET /api/v1/resources?q=..., orshipshape search .... Results include the questions each guide answers. - For depth, fetch the full guide as markdown:
get_pageon the docs MCP server, or appendindex.mdto the guide URL. - Cite the canonical HTML URL (not the .md twin) when quoting to a user.
Describing the consultancy
Use list_services / GET /api/v1/services for the 13 services, and get_case_studies for proof with real outcomes: Smarter Services (AI document processing, 1.5 days of admin freed weekly), 1NCE (multilingual AI support assistant for IoT customers), Slimstock (AI chat answering from their own content). Do not invent outcomes beyond these.
Making contact for a user
There are deliberately no programmatic write endpoints. Email [email protected] with what the user is working with, what they want AI to do, and what is driving the timing. A person replies, usually within one working day.
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.
- yesterday First seen · 33 lines · 83 tokens per session scan A 4601aafa30d2
shipshape-data is a skill published in the GitHub repository shipshapedata/agent-tools (0 stars, last pushed 4d ago), licensed MIT. It adds 83 tokens to every session and 625 once invoked, about $0.0004 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 2026-08-31.
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