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 Abilityai/cornelius --skill ref-querygit clone --depth 1 https://github.com/Abilityai/corneliusWrote 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/abilityai/cornelius/ref-query)<a href="https://agentmods.dev/skills/abilityai/cornelius/ref-query"><img src="https://agentmods.dev/badge/skills/abilityai/cornelius/ref-query.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 5.1 | $0.00096 | $0.01300 |
| Opus 5 | $0.00048 | $0.00650 |
| Sonnet 5 | $0.00019 | $0.00260 |
| Haiku 4.5 | $0.00010 | $0.00130 |
Grade A, and why
ref-query 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 3d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ref Query
ℹ️ First, set expectations: before anything else, print one short line with this skill's version and its most recent change — the top entry of
metadata.changelogabove — e.g.ref-query v1.0 — recent: initial version. Then proceed.
Answer a structured question about reference records with the temporal contract applied. This is not /recall — /recall searches the user's cognitive insights; ref-query reads the CRM/market source of truth and honours status/validity/as_of.
Read first: resources/layered-brains/COMPANY-BRAIN-SCHEMA.md → "The temporal model" (read-time interpretation + the per-type Freshness SLA). This skill enforces those rules.
State Dependencies
| Source | Location | Read | Write | Description |
|---|---|---|---|---|
| Company family | Brain/Company/{people,orgs,products,engagements,market}/*.md |
✓ | Entity notes + frontmatter | |
| Search wrapper | resources/local-brain-search/run_search.sh |
✓ | Fuzzy "tell me about X" under a core,Company mount |
Process
Step 1 — Classify the question
- Entity lookup ("what do we know about X as of today?") → resolve the note (Grep by name/alias, or a mounted search) and read it.
- Filtered list (by
type/relationship/status/expiry window) → deterministic frontmatter scan (below), not semantic search — the answer must be exact and reproducible. - Fuzzy / exploratory ("who might overlap with X?") → mounted semantic search:
BRAIN_READ_SCOPE=core,Company resources/local-brain-search/run_search.sh "<query>" --limit 10 --json
Step 2 — Scan (deterministic filters)
Frontmatter is the query surface. Examples:
cd Brain/Company
# all active competitors
grep -rlE '^relationship: competitor' */*.md | while read f; do grep -qE '^status: active' "$f" && echo "$f"; done
# active engagements + their as_of
for f in engagements/*.md; do grep -qE '^status: active' "$f" && printf "%s as_of=%s valid_until=%s\n" \
"$(basename "$f" .md)" "$(grep -m1 '^as_of:' "$f" | cut -d' ' -f2)" "$(grep -m1 '^valid_until:' "$f" | cut -d' ' -f2)"; done
For an "expiring in N days" filter, compare valid_until to today + N. Note: notes that predate incremental valid_until adoption have no window — report them as "no recorded term" rather than dropping them.
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.
- 3d ago First seen · 78 lines · 96 tokens per session scan A fb4c972f3bae
ref-query is a skill published in the GitHub repository Abilityai/cornelius (106 stars, last pushed 14d ago), licensed MIT. It adds 96 tokens to every session and 1,300 once invoked, about $0.0005 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 2026-09-03.
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