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 anishfyi/trove --skill querygit clone --depth 1 https://github.com/anishfyi/troveWrote 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/anishfyi/trove/query)<a href="https://agentmods.dev/skills/anishfyi/trove/query"><img src="https://agentmods.dev/badge/skills/anishfyi/trove/query/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/anishfyi/trove/query"><img src="https://agentmods.dev/badge/skills/anishfyi/trove/query.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00049 | $0.00308 |
| Opus 5.5 | $0.00020 | $0.00123 |
| Sonnet 5.5 | $0.00010 | $0.00062 |
| Haiku 4.5 | $0.00005 | $0.00031 |
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 1mo 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
Query (progressive retrieval)
Retrieve the smallest useful context for a task from the Trove index.
Steps
-
Check index exists. Run:
cargo run -p trove-cli -- statusIf no index, run /trove:index first.
-
Query with the user's intent:
cargo run -p trove-cli -- query "vendor onboarding"Adjust
--budget(default 200000 tokens) if the user specifies a context limit. -
Use the output progressively. The CLI returns layers in order:
- L4 Architecture
- L3 Subsystems
- L2 Modules
- L1 Symbols (signatures)
- Dependency neighbors
Stop expanding when you have enough detail. Do not open full source files unless summaries are insufficient.
-
JSON mode for structured parsing:
cargo run -p trove-cli -- query "auth middleware" --json
Each item includes confidence and retrieval path. Prefer higher-confidence, higher-layer items first.
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.
- 1mo ago First seen · 46 lines · 49 tokens per session scan A c4e17db5317a
query is a skill published in the GitHub repository anishfyi/trove (1 stars, last pushed 3d ago), licensed MIT. It adds 49 tokens to every session and 308 once invoked, about $0.0002 per session on Opus 5.5. 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
rethink
Challenge system assumptions against accumulated evidence. Triages observations and tensions, detects patterns, generates proposals. The scientific method applied to knowledge systems. Triggers on "/rethink", "review observations", "challenge assumptions", "what have I learned".
graph
Interactive knowledge graph analysis. Routes natural language questions to graph scripts, interprets results in domain vocabulary, and suggests concrete actions. Triggers on "/graph", "/graph health", "/graph triangles", "find synthesis opportunities", "graph analysis".
remember
Capture friction as methodology notes. Three modes — explicit description, contextual (review recent corrections), session mining (scan transcripts for patterns). Triggers on "/remember", "/remember [description]".
seed
Add a source file to the processing queue. Checks for duplicates, creates archive folder, moves source from inbox, creates extract task, and updates queue. Triggers on "/seed", "/seed [file]", "queue this for processing".
reflect
Find connections between notes and update MOCs. Requires semantic judgment to identify genuine relationships. Use after /reduce creates notes, when exploring connections, or when a topic needs synthesis. Triggers on "/reflect", "/reflect [note]", "find connections", "update MOCs", "connect these notes".
okf
Author, maintain, and consume Open Knowledge Format (OKF) knowledge bundles — portable markdown + YAML frontmatter that both humans and agents read. Use when capturing project knowledge (services, APIs, schemas, metrics, runbooks, decisions) into an OKF bundle, when updating one after code or docs change, or when a…