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/xcota/pos/querynpx skills add xcota/pos --skill querygit clone --depth 1 https://github.com/xcota/posWhat 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 | $0.00021 | $0.00890 |
| Opus 5 | $0.00010 | $0.00445 |
| Sonnet 5 | $0.00004 | $0.00178 |
| Haiku 4.5 | $0.00002 | $0.00089 |
Grade A, and why
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 2d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/query — Ask, Synthesize, File-Back
LLM-wiki pattern: every non-trivial query produces a new synthesis page. The wiki compounds.
Usage
/query {question} — ask and file
/query --ephemeral {question} — answer without filing (for trivial lookups)
When to Use
- Any question requiring synthesis across 2+ entities
- Strategic decisions ("should we X?")
- Comparative analysis ("difference between X and Y")
- How-to that combines multiple sources
- Anything you'd want to NOT re-derive next time
When NOT to Use
- Factual lookups ("what's X's email") — use grep
- Trivial questions ("how many entities")
- Clarifications within the current session
Pipeline
Step 1: Search Existing
grep -rl "{keywords}" knowledge/
Check whether the question (or a close variant) was already filed. If yes:
- Read the existing synthesis.
- Decide: augment, supersede, or refer.
Step 2: Gather Sources
Find relevant entities:
- Always check your domain hubs first — if your graph has a MOC or hub note for the question's domain (e.g.
knowledge/moc/{domain}.md), start there. - By tag:
grep -rl "domain/{X}" knowledge/ - By concept:
grep -rl "concept-name" knowledge/ - By person: check
knowledge/people/ - Relevant transcripts/sources: check
reports/
List sources explicitly — every claim needs a source.
Step 3: Synthesize
Produce an answer with:
- Direct answer first (results-first).
- Reasoning backed by evidence from sources.
- Each claim cites
[[source-entity]]. - Contradictions flagged explicitly.
- Uncertainty quantified.
Step 4: Score
Apply the context/scoring-gate.md criteria (≥80/100). If below: iterate.
Step 5: File Back
Create a new entity at knowledge/concepts/query_{slug}_{date}.md:
---
type: synthesis
tags: [domain/{X}, type/synthesis, query]
updated: {today}
question: "{original question}"
sources: [[entity1]], [[entity2]], ...
score: {0-100}
---
# {Question}
## Answer
{direct synthesis}
## Evidence
- From [[entity1]]: {quote/paraphrase}
- From [[entity2]]: {quote/paraphrase}
## Caveats
{what's uncertain, what's missing}
## Connections
- [[related-entity]]
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.
- 2d ago First seen · 107 lines · 21 tokens per session scan A 4cc8193c8ba3
query is a skill published in the GitHub repository xcota/pos (52 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 890 once invoked, about $0.0001 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 2026-08-30.
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