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/timurgaleev/memex/schema-unifynpx skills add timurgaleev/memex --skill schema-unifygit clone --depth 1 https://github.com/timurgaleev/memexWrote 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/timurgaleev/memex/schema-unify)<a href="https://agentmods.dev/skills/timurgaleev/memex/schema-unify"><img src="https://agentmods.dev/badge/skills/timurgaleev/memex/schema-unify.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 | $0.00085 | $0.02607 |
| Opus 5 | $0.00043 | $0.01303 |
| Sonnet 5 | $0.00017 | $0.00521 |
| Haiku 4.5 | $0.00009 | $0.00261 |
Grade A, and why
schema-unify 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 5d 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 — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Schema Unification (noisy types → canonical taxonomy)
The brain ships a 15-type DRY/MECE canonical taxonomy (14 canonical +
note catch-all), recorded on the concepts/type-conventions page. Brains
that accreted noisy per-source types over years of ingestion opt in via the
doctor's type-proliferation finding + a durable unify-types job that is
PROTECTED (never auto-fired by the background cycle).
This skill is the playbook for that migration.
brain_first: exempt
This skill is ABOUT the brain's shape — it can't depend on the brain it's
reshaping. No search lookup first; jump straight to the doctor.
When this skill fires
- Agent runs
run_doctor(ormemex doctoron the host) and sees a type-proliferation or dangling-alias warning - User asks "what is the canonical taxonomy / how do I clean up my page types / migrate to canonical"
- A dangling-aliases finding surfaces (post-unify GC)
- An agent ingesting from a custom source wants to consult the canonical taxonomy as a reference
Mental model (one paragraph)
A long-lived brain accretes dozens of distinct frontmatter type values
over years of ingestion: tweet / tweet-thread / tweet-bundle / tweet-single /
media/x-tweet/bundle / tweet-stub all coexisting; thousands of
concept-redirect pages; partner-link pages that should be links; civic /
framework / insight / memo / anecdote one-offs. The cure: collapse to 15
canonical types (person, company, media, tweet, social-digest, analysis,
atom, concept, source, deal, email, slack, writing, project, note) with
subtype/format/origin pushed to frontmatter, slug-alias rows for redirects,
real link rows for edge-shaped pages, and a catch-all that bins long-tail
unknowns to note with frontmatter.legacy_type = <original> for rollback.
Workflow
Phase 1: Discovery
Confirm the brain actually has proliferation (not already canonical).
ontology_get
If the observed type set is already ≤16 distinct values matching the
canonical list on concepts/type-conventions, the brain is already unified —
skip the migration.
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.
- 5d ago First seen · 301 lines · 85 tokens per session scan A f80e75e05cbc
schema-unify is a skill published in the GitHub repository timurgaleev/memex (8 stars, last pushed 3d ago), licensed MIT. It adds 85 tokens to every session and 2,607 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.
Other skills, from other repositories
api-canvas
DataCanvas primitive reference — a Tier 3 SQL/analytical workspace for tabular MCP servers, backed by DuckDB. Use when registering tables from upstream APIs, running ad-hoc SQL across them, and exporting results. Covers the acquire → register → query → export flow, per-table TTL, the token-sharing pattern for…
api-mirror
Stand up a persistent, self-refreshing local mirror of a bulk upstream dataset with the MirrorService (@cyanheads/mcp-ts-core/mirror). Use when a server wraps a large or slow API and should query a synced local index (embedded SQLite + FTS5) instead of paginating the live API per request.
api-canvas
DataCanvas primitive reference — a Tier 3 SQL/analytical workspace for tabular MCP servers, backed by DuckDB. Use when registering tables from upstream APIs, running ad-hoc SQL across them, and exporting results. Covers the acquire → register → query → export flow, per-table TTL, the token-sharing pattern for…
api-canvas
DataCanvas primitive reference — a Tier 3 SQL/analytical workspace for tabular MCP servers, backed by DuckDB. Use when registering tables from upstream APIs, running ad-hoc SQL across them, and exporting results. Covers the acquire → register → query → export flow, per-table TTL, the token-sharing pattern for…
api-canvas
DataCanvas primitive reference — a Tier 3 SQL/analytical workspace for tabular MCP servers, backed by DuckDB. Use when registering tables from upstream APIs, running ad-hoc SQL across them, and exporting results. Covers the acquire → register → query → export flow, per-table TTL, the token-sharing pattern for…
api-canvas
DataCanvas primitive reference — a Tier 3 SQL/analytical workspace for tabular MCP servers, backed by DuckDB. Use when registering tables from upstream APIs, running ad-hoc SQL across them, and exporting results. Covers the acquire → register → query → export flow, per-table TTL, the token-sharing pattern for…