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 BeppeTemp/cartographer --skill cartographer-kb-ingestgit clone --depth 1 https://github.com/BeppeTemp/cartographerWrote 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/beppetemp/cartographer/cartographer-kb-ingest)<a href="https://agentmods.dev/skills/beppetemp/cartographer/cartographer-kb-ingest"><img src="https://agentmods.dev/badge/skills/beppetemp/cartographer/cartographer-kb-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/beppetemp/cartographer/cartographer-kb-ingest"><img src="https://agentmods.dev/badge/skills/beppetemp/cartographer/cartographer-kb-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 | $0.00079 | $0.01690 |
| Opus 5.5 | $0.00032 | $0.00676 |
| Sonnet 5.5 | $0.00016 | $0.00338 |
| Haiku 4.5 | $0.00008 | $0.00169 |
Grade A, and why
cartographer-kb-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 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KB Ingest — Skill
Purpose
Absorb one new primary source into knowledge the KB already holds. Unlike cartographer-kb-import, which
maps the structure of a whole foreign corpus with a mechanical scaffold, ingestion takes one
unstructured source and merges its content into existing pages. The agent reads the source;
the server never fetches or parses anything (D28, amended by D278). Binary formats (PDF, DOCX,
images) are read with the client's own capabilities. The procedure is the same for every kind of
source; kind-specific advice is in references/source-kinds.md.
Without this procedure the usual failures are: the same source ingested twice, new pages where a patch belonged, existing claims silently overwritten, unknowns dropped, and no trace of which pages came from which source.
When to use
A new document, transcript, thread, ticket, incident timeline, email, web page or dataset must
become KB knowledge. Not for importing a whole wiki (cartographer-kb-import), creating a KB (cartographer-kb-create)
or server operations (cartographer-ops).
Procedure
1. Identify
Kind, title, date, locator: a URL or a {{path:<key>}} placeholder, never a raw machine path.
When the source is a file, compute the sha256 of the original bytes with the client's shell
(e.g. shasum -a 256 <file>), lowercase hex.
2. Register
source_register(title, source_kind, locator?, sha256?, timestamp?). The ledger journal
(sources by default) must exist: map_create(name: "sources", kind: "journal") if it does not.
duplicate: truewithingest_status: ingested→ stop and tell the operator.duplicate: truewithingest_status: pending→ resume that Source (use the returnedid).source_listshows what else is pending.
3. Distil
Write the distillation into the Source's body with concept_patch: key facts, decisions,
entities, dates, open questions. Every item carries a pointer back into the source (section,
timestamp, message, row) so a reader can verify it. For a long source, work section by section
and patch the body per section.
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.
- yesterday Changed · +1 lines 09bd7a698a5a
- 2d ago First seen · 111 lines · 79 tokens per session scan A 43f1330b6966
cartographer-kb-ingest is a skill published in the GitHub repository BeppeTemp/cartographer (45 stars, last pushed today), licensed Apache-2.0. It adds 79 tokens to every session and 1,690 once invoked, about $0.0003 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 2026-10-23.
Other skills, from other repositories
openlore
Query and publish to an OpenLore knowledge repository over SSH using ordinary shell commands. Use when a task needs project documentation, runbooks, shared team knowledge, or a place to publish findings.
openlore-housekeeping
Audit and maintain a shared OpenLore knowledge repository. Use on a schedule or on request to find stale docs, broken links, unreviewed inbox items, and missing skill coverage, then publish an audit report.
lemmalog
Externalize working memory and logical state into the lemmalog Datalog engine (MCP). Use for ANY multi-step task where state should outlive one context window or span agents: long investigations, debugging sessions, audits, multi-agent searches, systematic explorations, planning with many interdependent constraints…
causal-memory
Causal memory for agents — install/setup the causal-memory MCP server, then record decisions/outcomes and recall them before acting. Trigger when the user asks to install or set up causal-memory/agent memory, when causal-memory MCP tools are available and the agent faces a non-trivial decision (architecture, debugging…
prolong
Recover and use durable coding-session history from PRO-LONG's local append-only log. Use on long-running coding tasks, after context compaction or session resume, when reconstructing prior decisions or tool results, or before repeating work that may already have been attempted.
kb-memory
Local-first agent memory and knowledge service (RAG) installed from PyPI. Use when a task needs to store or recall long-term user preferences, project decisions, facts, documents or web pages, when different projects need separate memories, or when the user mentions "kb memory", "kb", or a local MCP memory service.…