buyer-job-intent-analysis

buyer-job-intent-analysis is a skill for Claude Code, Codex from elvisun/newsjack. It costs 73 tokens per session (1,264 once invoked), scanned A, original, MIT.

A research method for finding what buyers are genuinely trying to accomplish, what frustrates them, and how they describe those situations. It uses approved customer, market, search, review, forum, support, and procurement evidence rather than guessing demand from product features.

In plain words
What is it for?
Use it to analyze approved ideal-customer hypotheses alongside interviews, queries, reviews, support material, public discussions, RFPs, and other permitted market sources. It helps identify jobs, constraints, decision criteria, roles, stages, workarounds, and the language people actually use.
Why use it?
It helps prevent invented customer needs and unsupported marketing language. Source records, permissions, and collection details keep conclusions tied to evidence.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to analyze approved ideal-customer hypotheses alongside interviews, queries, reviews, support material, public discussions, RFPs, and other permitted market sources. It helps identify jobs, constraints, decision criteria, roles, stages, workarounds, and the language people actually use.

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Install with agentmods
npx agentmods add skills/elvisun/newsjack/buyer-job-intent-analysis
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.

Any agent
npx skills add elvisun/newsjack --skill buyer-job-intent-analysis
Clone the repo
git clone --depth 1 https://github.com/elvisun/newsjack

Made for: Claude Code, Codex.

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 buyer-job-intent-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/elvisun/newsjack/buyer-job-intent-analysis/github.svg)](https://agentmods.dev/skills/elvisun/newsjack/buyer-job-intent-analysis)
Your own site
<a href="https://agentmods.dev/skills/elvisun/newsjack/buyer-job-intent-analysis"><img src="https://agentmods.dev/badge/skills/elvisun/newsjack/buyer-job-intent-analysis/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 buyer-job-intent-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/elvisun/newsjack/buyer-job-intent-analysis"><img src="https://agentmods.dev/badge/skills/elvisun/newsjack/buyer-job-intent-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 73 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,264 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00073 $0.01264
Opus 5 $0.00036 $0.00632
Sonnet 5 $0.00015 $0.00253
Haiku 4.5 $0.00007 $0.00126

Measured 13d ago against content hash 4400d2fd0aa3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

buyer-job-intent-analysis 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 13d 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.

skills/buyer-job-intent-analysis/SKILL.md · 135 lines

How it starts

The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Buyer Job Intent Analysis

Recover what people are trying to accomplish and how they express it. Do not turn product features into imagined demand.

This skill inherits the ethical floor from skills/ETHICS.md. It enforces anti-hallucination, permission, provenance, and decay-aware handling of current market evidence. Anti-spray and human-send are not applicable.

Inputs

Require:

  • approved icp_hypotheses.json;
  • source_manifest.json with permission and provenance;
  • any user-supplied transcripts, queries, reviews, support material, or market sources.

Research missing public-market language when permitted. Prefer evidence in this order:

  1. lawfully collected relevant AI conversations with collection metadata;
  2. customer or prospect interviews and calls;
  3. on-site search, support, chat, sales, and win/loss evidence;
  4. paid-search, Search Console, marketplace, and site-search queries;
  5. public reviews, forums, communities, RFPs, procurement guides, and competitor reviews;
  6. broad search and People Also Ask proxies;
  7. company copy;
  8. LLM expansion.

Public conversational corpora may inform style, turn count, and multilingual naturalness. They must not supply category prevalence or copied prompts.

Grade evidence

Grade Meaning Eligible use
A Direct relevant behavior or verbatim customer/prospect language with provenance Wording and exposure-weight inputs
B Credible public-market behavior or search proxy with provenance Wording with an explicit proxy label
C Company assertion or expert hypothesis Research hypothesis; approval required
D LLM-generated expansion without independent support Rotating discovery only

A count is a count within the supplied corpus. Never relabel it market frequency.

Extract jobs and language

For each supported ICP, extract:

  • struggling moment or trigger;
  • desired progress or outcome;
  • current workaround;
  • push, pull, anxiety, and habit forces;
  • requested action or information need;
  • decision criteria, constraints, and proof sought;
  • exploration/evaluation state;
  • authentic source-language samples;
  • role/persona and locale when known;
  • contradictory, negative, or post-purchase evidence.

Read the full file on GitHub · 135 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. 13d ago First seen · 135 lines · 73 tokens per session scan A 4400d2fd0aa3

Subscribe to this mod's changes

buyer-job-intent-analysis is a skill published in the GitHub repository elvisun/newsjack (667 stars, last pushed 10d ago), licensed MIT. It adds 73 tokens to every session and 1,264 once invoked, about $0.0004 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.

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