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 Pranjay-kumar/universal-data-acquisition-pipeline-skill --skill data-acquisition-publishgit clone --depth 1 https://github.com/Pranjay-kumar/universal-data-acquisition-pipeline-skillWrote 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/pranjay-kumar/universal-data-acquisition-pipeline-skill/data-acquisition-publish)<a href="https://agentmods.dev/skills/pranjay-kumar/universal-data-acquisition-pipeline-skill/data-acquisition-publish"><img src="https://agentmods.dev/badge/skills/pranjay-kumar/universal-data-acquisition-pipeline-skill/data-acquisition-publish/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/pranjay-kumar/universal-data-acquisition-pipeline-skill/data-acquisition-publish"><img src="https://agentmods.dev/badge/skills/pranjay-kumar/universal-data-acquisition-pipeline-skill/data-acquisition-publish.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00062 | $0.00217 |
| Opus 5.5 | $0.00025 | $0.00087 |
| Sonnet 5.5 | $0.00012 | $0.00043 |
| Haiku 4.5 | $0.00006 | $0.00022 |
Grade A, and why
data-acquisition-publish 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 2mo 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.
What it actually says
Data Acquisition Publish
Act as the publication editor for real acquisition results. Publish only evidence-backed work.
Shared Core
Read from ../data-acquisition-core/references/:
source-access.mdoutput-contracts.mdfeasibility-scoring.mdcompliance-boundaries.md
Publication Rules
- Publish only real probes and documented evidence.
- Mark
owned_session,provided_credentials,licensed_api,partner_api, andinternal_systemoutputs as non-public unless terms explicitly allow publication. - Never publish cookies, tokens, storage state, private data, or screenshots containing secrets.
- Keep hypothetical examples in prompts or references, not
case-studies/.
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.
- 2mo ago First seen · 25 lines · 62 tokens per session scan A 0a2d687cd9df
data-acquisition-publish is a skill published in the GitHub repository Pranjay-kumar/universal-data-acquisition-pipeline-skill (3 stars, last pushed 4mo ago), licensed MIT. It adds 62 tokens to every session and 217 once invoked, about $0.0002 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 2026-08-31.
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Plan how to earn customer trust and appropriate access. Use when credibility, permissions or AI policy constrain the engagement.