research

A search command for finding text across an indexed collection of company earnings-call transcripts and articles. Earnings transcripts are written records of discussions in which companies report financial results.

In plain words
What is it for?
It helps search for topics, group matching passages by company, identify dates, and attribute quotations to speakers.
Why use it?
It helps find relevant passages without manually opening every transcript or article, while making clear that the search is limited to its index.

Command

Install

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.

agentmods
npx agentmods add commands/moshcoder/moshcode/research
Clone the repo
git clone --depth 1 https://github.com/moshcoder/moshcode
Per session 11 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 253 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00011 $0.00253
Opus 5 $0.00005 $0.00127
Sonnet 5 $0.00002 $0.00051
Haiku 4.5 $0.00001 $0.00025

Measured yesterday against content hash ec90861ee61a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

research scanned grade A with 1 finding 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 yesterday.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

allowed-tools: Bash(moshcode stocks:*), Bash(curl -sS https://advis0r.com/api/:*)
plugins/stocks/commands/research.md · 30 lines

What it actually says

Task

Search the transcript index for $ARGUMENTS.

moshcode stocks search $ARGUMENTS --limit 20 --json

Fallback: curl -sS "https://advis0r.com/api/search?q=<url-encoded>&limit=20"

Reading the response

results is a list of segments: text, speaker, ticker, event_date. The API tries full-text search first and falls back to a substring scan, so a hit is a hit — but relevance is not ranked. Read before summarizing.

Rules

  • Cluster the hits by ticker and say which companies came up, with dates.
  • Quote sparingly and attribute each quote to its speaker and ticker.
  • If nothing matches, say the index has no match — this searches advis0r's indexed corpus, not the whole web. Suggest /stocks:lookup if the query looks like a company name.
Changes

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.

  1. yesterday First seen · 30 lines · 11 tokens per session scan A ec90861ee61a

Subscribe to this mod's changes

research is a command published in the GitHub repository moshcoder/moshcode (2 stars, last pushed yesterday), licensed MIT. It adds 11 tokens to every session and 253 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.