harvest-skeptic

harvest-skeptic is an agent for Claude Code from Pupok462/open-geo. It costs 53 tokens per session (839 once invoked), scanned A, original, MIT.

An adversarial review role for checking a proposed list of questions before it is used in an AI-visibility measurement. It marks each question KEEP or CUT and explains the decision.

In plain words
What is it for?
Use it to test whether questions sound like things real people would ask, check their categories, and return review decisions without editing files or running the capture.
Why use it?
It removes invented, duplicate, or badly labelled questions that could make the measurement less credible.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the open-geo plugin — 2 skills, 4 agents shipped together

Good fit Use it to test whether questions sound like things real people would ask, check their categories, and return review decisions without editing files or running the capture.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/pupok462/open-geo/harvest-skeptic
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.

Clone the repo
git clone --depth 1 https://github.com/Pupok462/open-geo

Made for: Claude Code.

Or install open-geo, the plugin that ships this one along with the rest of its 2 skills, 4 agents.

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 harvest-skeptic

README.md
[![agentmods](https://agentmods.dev/badge/agents/pupok462/open-geo/harvest-skeptic/github.svg)](https://agentmods.dev/agents/pupok462/open-geo/harvest-skeptic)
Your own site
<a href="https://agentmods.dev/agents/pupok462/open-geo/harvest-skeptic"><img src="https://agentmods.dev/badge/agents/pupok462/open-geo/harvest-skeptic/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.

agentmods 80×15 button for harvest-skeptic

Your own site · 80×15
<a href="https://agentmods.dev/agents/pupok462/open-geo/harvest-skeptic"><img src="https://agentmods.dev/badge/agents/pupok462/open-geo/harvest-skeptic.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 53 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 839 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00053 $0.00839
Opus 5 $0.00026 $0.00419
Sonnet 5 $0.00011 $0.00168
Haiku 4.5 $0.00005 $0.00084

Measured 6d ago against content hash ed5dac7d8f21, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

harvest-skeptic 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 6d 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.

.agentsmesh/agents/harvest-skeptic.md · 53 lines

How it starts

The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.

harvest-skeptic — adversarial question reviewer

You receive a final candidate question set and try to break it. Your job is to keep only queries a real person would actually ask an assistant, correctly labeled. You are spawned by the open-geo orchestrator (question-sourcing, SKILL STEP A.5, Phase C). You do not edit files, write questions.csv, or run anything — you return verdicts.

What you receive (spawn brief)

  • The thesis / product context (brand, domain, market, what the product does).
  • The final set as a list of {query, lens} (optionally with segment/signal).
  • Pointers: harvest/METHODOLOGY.md (§3 iron rule, §4 lens invariants) and pipeline/INTERFACES.md §6.

What you must do

Judge every line KEEP or CUT. Default to CUT when unsure — a shipped query that reads as invented poisons the audit's credibility (moat #3). Cut a line for any of:

  • Invented / no real signal — you cannot believe a real person phrases it this way; searching turns up no such pattern. (You may spot-check with WebSearch, but the burden is on the query.)
  • Meaning-duplicate — it says the same thing as another kept line; keep the single strongest.
  • Wrong lens — brand named in a general line; brand absent from a branded line; no comparison in a comparative line (INTERFACES §6 / METHODOLOGY §4).
  • Off-thesis — it would read as a false low-visibility result (e.g. asks for something the product is not, so the brand's absence is expected and misleading), or drifts off the product's real needs.
  • Keyword-stuffed / unnatural — reads like an SEO key, not a person talking to an assistant.
  • Unmeasured line — the signal carries neither a demand-provider scope string (a demand/ pull: region, period, pull date) nor a stated reason why no ruler applies to that line (METHODOLOGY §3, demand gate). You do not re-pull the number yourself; judge the claim: a bare "wordstat: 1200" with no scope, or a figure with no provider behind it, is unmeasured.
  • Presence-only line carrying a volume claim — the signal says presence only, no volume (autocomplete) but the line is argued as if demand were measured. Presence is a real signal and keeps the line eligible; overstating it is not.

Read the full file on GitHub · 53 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. 6d ago Changed · +7 lines ed5dac7d8f21
  2. 11d ago First seen · 46 lines · 53 tokens per session scan A aca961883766

Subscribe to this mod's changes

harvest-skeptic is an agent published in the GitHub repository Pupok462/open-geo (25 stars, last pushed 6d ago), licensed MIT. It adds 53 tokens to every session and 839 once invoked, about $0.0003 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.

Related

Other agents, from other repositories

geo-schema

Schema markup specialist detecting, validating, and generating structured data (JSON-LD preferred). Focuses on schemas that improve AI discoverability including Organization, Person, Article, sameAs, and speakable properties.

Cognitic-Labs/geoskills · 44 tokens

geo-citability

AI citability scoring and optimization specialist. Analyzes how likely AI systems are to cite, quote, or reference content from a website. Evaluates answer block quality, self-containment, statistical density, structural clarity, and expertise signals.

Cognitic-Labs/geoskills · 53 tokens

geo-brand

Brand and entity signal specialist analyzing Wikipedia/Wikidata presence, third-party platform profiles, community mentions, and cross-source brand consistency for AI entity recognition.

Cognitic-Labs/geoskills · 33 tokens

geo-technical

Technical SEO specialist analyzing crawlability, indexability, security, URL structure, mobile optimization, Core Web Vitals (INP replaces FID), server-side rendering, and JavaScript dependency.

Cognitic-Labs/geoskills · 42 tokens

geo-content

Content quality specialist evaluating E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness), content depth, readability, AI content detection, and topical authority.

asale-ai/seo-geo-skill · 37 tokens

geo-schema

Schema markup specialist detecting, validating, and generating structured data (JSON-LD preferred). Focuses on schemas that improve AI discoverability including Organization, Person, Article, sameAs, and speakable properties.

asale-ai/seo-geo-skill · 44 tokens