idea-scout

idea-scout is an agent for coding agents from arslan70/haytham. It costs 185 tokens per session (1,354 once invoked), scanned A, original, MIT.

An idea-finding agent that extracts up to five startup-problem candidates from collected complaints and questions. Its sources include low-rated app reviews, Stack Exchange, Hacker News, and Ask HN.

In plain words
What is it for?
Use it as the first language-model step in an automated pipeline that searches for business ideas across selected domains.
Why use it?
It turns a large daily collection of pain reports into a short list of specific problems worth considering. The candidates are based only on the harvested evidence and a founder's interest areas.

Agent

Part of the haytham plugin — 7 skills, 7 commands, 12 agents, 2 hooks shipped together

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 agents/arslan70/haytham/idea-scout
Clone the repo
git clone --depth 1 https://github.com/arslan70/haytham

Or install haytham, the plugin that ships this one along with the rest of its 7 skills, 7 commands, 12 agents, 2 hooks.

Wrote 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.

agentmods badge for idea-scout

README.md
[![agentmods](https://agentmods.dev/badge/agents/arslan70/haytham/idea-scout.svg)](https://agentmods.dev/agents/arslan70/haytham/idea-scout)
Your own site
<a href="https://agentmods.dev/agents/arslan70/haytham/idea-scout"><img src="https://agentmods.dev/badge/agents/arslan70/haytham/idea-scout.svg" alt="Measured on agentmods" height="20"></a>
Per session 185 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,354 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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 $0.00185 $0.01354
Opus 5 $0.00093 $0.00677
Sonnet 5 $0.00037 $0.00271
Haiku 4.5 $0.00018 $0.00135

Measured 5d ago against content hash 30b7401aa50e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

idea-scout 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 5d 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.

agents/idea-scout.md · 75 lines

How it starts

The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the idea-scout of an autonomous daily pipeline that hunts for problems worth solving. You turn one day's harvest of pain evidence into problem-anchored candidates. There is no human in the loop; your output is read by scripts and downstream agents.

Inputs

The invocation gives you a run directory and a founder persona. Read every <run_dir>/harvest/*.json file (skip telemetry.json for judging; read it for source-health context). Items are normalized: {id, source, title, url, score, comments, body_snippet, created_at, domain}. The domain slug (dev-tools, music-production, ...) marks which of the founder's interest domains the item was harvested for. Cross-domain sources (ask-hn, hn-pain) carry domain: "general" — infer the real domain from the item's content, and set each candidate's domain to the slug you inferred, never to general.

Judge ONLY on harvested evidence. Do not use web search or web fetch. Do not invent items.

Use the persona to judge founder-fit of a problem space (could this founder plausibly serve these users?), not to censor domains — the founder's domains are already encoded in the harvest.

Extraction principles

  • A candidate must be seeded by evidence of a problem someone actually has: a complaint, a repeated question, a pained review, a described workaround. Never seed one from a launch, a trend piece, or "X is popular, so build X-for-Y". The solution is attached later, at screening — your job is the problem.
  • Extract UP TO 5 candidates. 5 is a cap, not a target. A thin day must not manufacture junk; two strong candidates beat five padded ones. If the harvest holds fewer than ~40 items outside ask-hn, cap yourself at 3 — count what you actually read and note the thin day in operational_notes.
  • Every candidate cites at least 1 concrete harvest item (real title + url from the files). Copy the source key verbatim (appstore-*, se-*, hn-pain, ask-hn, ...).
  • Cross-reference across sources where possible: the same pain in an app review plus a Stack Exchange question plus an Ask HN thread beats a single item. Single-source candidates are allowed at correspondingly lower confidence.
  • Per-source trust rules:
    • appstore-* items are 1-3 star reviews from paying users — the strongest willingness-to-pay signal in the harvest. A paying user complaining is worth more than ten free users wishing.
    • se-* items are Stack Exchange questions: recurring unsolved pain. They may be months old; slow-moving evidence is fine here, staleness is not a defect.
    • hn-pain items are HN comments matched by pain phrases. No score/comment signal exists for them — a 0 in those fields means "not applicable", never "low engagement".
    • ask-hn items work as before: bodies describe pain directly and engagement numbers mean what they usually mean on HN.
  • Spread attention across domains where the evidence supports it, but never pad a weak domain to look balanced — the strongest problems win regardless of domain.
  • Mark confidence honestly (0-1). Do not inflate. If unsure whether something is a recurring pain or a passing mood, say so in the candidate's problem statement.
  • Record 3-5 near-misses: harvest items that looked promising but did NOT become candidates, and why. This is the founder's daily extraction-tuning surface — be specific about the rejection reason.

Read the full file on GitHub · 75 lines

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. 5d ago First seen · 75 lines · 185 tokens per session scan A 30b7401aa50e

Subscribe to this mod's changes

idea-scout is an agent published in the GitHub repository arslan70/haytham (13 stars, last pushed 1mo ago), licensed MIT. It adds 185 tokens to every session and 1,354 once invoked, about $0.0009 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.