Newsjack.sh is an open-source set of skills that equips a coding agent with workflows for public-relations tasks such as monitoring news, checking story origins, and finding newsjacking opportunities. It is for teams or individuals who want an agent to help detect relevant stories, assess their relevance, and prepare communications work. The catalogue entries are Newsjack.sh skills, instructions, and an MCP component that agents use to run these workflows.
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 elvisun/newsjack --skill buyer-job-intent-analysisgit clone --depth 1 https://github.com/elvisun/newsjackWrote 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/elvisun/newsjack/buyer-job-intent-analysis)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00073 | $0.01272 |
| Opus 5.5 | $0.00029 | $0.00509 |
| Sonnet 5.5 | $0.00015 | $0.00254 |
| Haiku 4.5 | $0.00007 | $0.00127 |
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 25d 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.
How it starts
The opening of the file, as written. The whole thing — 137 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.jsonwith 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:
- lawfully collected relevant AI conversations with collection metadata;
- customer or prospect interviews and calls;
- on-site search, support, chat, sales, and win/loss evidence;
- paid-search, Search Console, marketplace, and site-search queries;
- public reviews, forums, communities, RFPs, procurement guides, and competitor reviews;
- broad search and People Also Ask proxies;
- company copy;
- 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.
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.
- 25d ago Changed · +2 lines 94a7ebde429e
- 1mo ago First seen · 135 lines · 73 tokens per session scan A 4400d2fd0aa3
buyer-job-intent-analysis is a skill published in the GitHub repository elvisun/newsjack (1,658 stars, last pushed 2d ago), licensed MIT. It adds 73 tokens to every session and 1,272 once invoked, about $0.0003 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 2026-08-30.
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