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 commands/trapoom555/claude-paperloom/querygit clone --depth 1 https://github.com/trapoom555/claude-paperloomWrote 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/commands/trapoom555/claude-paperloom/query)<a href="https://agentmods.dev/commands/trapoom555/claude-paperloom/query"><img src="https://agentmods.dev/badge/commands/trapoom555/claude-paperloom/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 | $0.00023 | $0.00395 |
| Opus 5 | $0.00012 | $0.00198 |
| Sonnet 5 | $0.00005 | $0.00079 |
| Haiku 4.5 | $0.00002 | $0.00040 |
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 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.
What it actually says
/paperloom:query
$ARGUMENTS is a natural-language question about the vault's contents.
Vault path
Read vault_path from ${CLAUDE_PLUGIN_CONFIG:vault_path} (default ~/PaperLoom), expand ~ to an absolute path, and use that for every Read/Grep/Glob.
Steps
-
Plan the search. Identify likely fields/authors/claim types from the question (e.g. "transformer complexity" → field
nlp,complexity-analysis). Note key entities. -
Scan:
- grep
<vault>/papers/and<vault>/findings/for the key terms (titles, statements, frontmatter values). - Read the top candidates (papers: Key Takeaways + relevant sections; findings: statement + evidence).
- grep
-
Synthesize. Answer the question. Cite every factual statement with a wikilink — e.g.
[[2017-06-attention-is-all-you-need]]or[[finding-self-attention-is-O-n2]]. If findings conflict, say so and cite both sides. -
Log via the shared script:
"${CLAUDE_PLUGIN_ROOT}/.venv/bin/python3" "${CLAUDE_PLUGIN_ROOT}/scripts/log.py" "<vault-path>" query "<one-line question>" "cited <n> files"
Guardrails
- Never fabricate a citation. If you can't find evidence, say so and suggest which paper to ingest.
- Prefer finding-level wikilinks over paper-level when a specific finding is relevant.
- Keep answers tight. The user drills into the vault via the citations.
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 · 32 lines · 23 tokens per session scan A 5a34556d0752
query is a command published in the GitHub repository trapoom555/claude-paperloom (95 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 23 tokens to every session and 395 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.
Other commands, from other repositories
wiki-graph
Build the LLM Wiki knowledge graph.
wiki-ingest
Ingest a source document into the LLM Wiki.
wiki-lint
Health-check the LLM Wiki for issues.
wiki-query
Query the LLM Wiki and synthesize an answer.
kg-ingest
Ingest a source document into the Knowledge Graph - extract entities, concepts, create wiki pages.
kg-init
Initialize a new Knowledge Graph with raw/ and wiki/ structure.