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/hellothisworld/open-mind/wikipedia-glossarynpx skills add HelloThisWorld/open-mind --skill wikipedia-glossarygit clone --depth 1 https://github.com/HelloThisWorld/open-mindWrote 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/hellothisworld/open-mind/wikipedia-glossary)<a href="https://agentmods.dev/skills/hellothisworld/open-mind/wikipedia-glossary"><img src="https://agentmods.dev/badge/skills/hellothisworld/open-mind/wikipedia-glossary.svg" alt="Measured on agentmods" 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 | $0.00091 | $0.02287 |
| Opus 5 | $0.00046 | $0.01144 |
| Sonnet 5 | $0.00018 | $0.00457 |
| Haiku 4.5 | $0.00009 | $0.00229 |
Grade A, and why
wikipedia-glossary scanned grade A with 1 finding 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 4d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST "{base}/glossary/enrich" -H "Content-Type: application/json" \ How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wikipedia glossary enrichment
Open Mind extracts glossary terms verbatim from a project's own authoritative sources and never fabricates them. This skill adds an authoritative standard definition for those terms from Wikipedia — kept strictly separate from, and never replacing, the in-project definition.
How enrichment runs (pipeline guarantee + this portable skill)
Open Mind now runs enrichment automatically as a deterministic pipeline step:
after a project is learned (and on every server start via a recovery reconciler),
the in-app engine (openmind/wikienrich.py) looks up un-attempted terms and writes
results — so enrichment is guaranteed without depending on an agent remembering
to act. Wikipedia egress is allowed but audited (every call is logged to
/netlog); only the enrichment path may egress, and only single glossary terms are
sent — never project source.
This skill is the portable, manual/agentic interface to the same capability:
run it to (re)enrich on demand, change the disambiguation context, pin a domain
acronym, or enrich from a runtime that isn't the Open Mind app (Codex,
open-claw, etc.). It writes back via POST /glossary/enrich; the in-app engine and
this script share the same conservative matching. Use it when you want manual
control or an override — the pipeline covers the automatic case.
Two honesty guarantees are enforced on the Open Mind side:
- No invented terms. A standard definition is only attached to a term the
project's own sources already surfaced. An unknown term is reported under
missing, never created. - Verbatim definition untouched. The in-project definition and its
file:lineprovenance are preserved; the Wikipedia text is a distinct field, labelled and footnoted in the UI ("Open Mind used its search skill to extract the above from Wikipedia and tidy it").
When to use
- The user asks to enrich / add standard / authoritative / Wikipedia definitions to a project's glossary, or runs this skill by name.
- Typically right after a project finishes learning (its terms are extracted) and the user wants richer definitions than the project's own sources provide.
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.
- 4d ago First seen · 186 lines · 91 tokens per session scan A 6befd14645a7
wikipedia-glossary is a skill published in the GitHub repository HelloThisWorld/open-mind (1 stars, last pushed 1mo ago), licensed MIT. It adds 91 tokens to every session and 2,287 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
codestory-grounding
Use when an agent should ground a local repository with CodeStory before making source claims, planning edits, choosing tests, reviewing changes, or using broad retrieval evidence through the CodeStory plugin MCP.
memtrace-first
Route code discovery, debugging, flow tracing, how-code-works questions, and pre-edit rationale checks in indexed source-code repos to Memtrace graph plus Cortex decision tools. Use first before searching/reading code, and before editing, refactoring, deleting, or re-picking an approach that may have a recorded…
memtrace-style-fingerprint
Pull the codebase's empirical style norm from Memtrace and match it when writing or editing source code in an indexed repo. Use when choosing between competing idioms (ternary vs if-else, arrow vs function declaration, const vs let, await vs .then, early-return vs nested-return), matching naming case, or when the user…
memtrace-decision-memory
Use Cortex decision memory through the normal Memtrace MCP tools. Trigger for free-text questions about what was decided, chosen, rejected, banned, or established as a convention; for why a symbol exists or which contracts constrain it; for whether a known decision held, drifted, or was violated; and for the…
memtrace-docs
Use official hosted Memtrace documentation before guessing, web search, or stale local copies. Trigger when the user asks how Memtrace works; how to install, configure, or deploy CLI, MCP, fleet, Cortex, or enterprise MemDB; what tools, skills, or commands exist; wants to find or locate official docs for a topic; or…
memtrace-evolution
Trace source-code change history from Memtrace's symbol-level temporal memory. Use when the user asks about change history, recent modifications, what changed since a date, symbol timeline, evolution, unexpected changes, or incident timelines. Do not use git log, git diff, Grep, or manual file search to reconstruct…