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 yogsoth-ai/de-anthropocentric-research-engine --skill formated-resultsgit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWrote 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/yogsoth-ai/de-anthropocentric-research-engine/formated-results)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/formated-results"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/formated-results/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/yogsoth-ai/de-anthropocentric-research-engine/formated-results"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/formated-results.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 | $0.00047 | $0.00311 |
| Opus 5 | $0.00023 | $0.00156 |
| Sonnet 5 | $0.00009 | $0.00062 |
| Haiku 4.5 | $0.00005 | $0.00031 |
Grade A, and why
formated-results 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 11d 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
formated-results
You are the closing step, loaded by formated-specs. Summarize the design you
just produced into one fenced block, in the same dialogue. Add no new
research — only summarize what formated-specs already orchestrated.
Hard contract
- Emit exactly one fenced block opened with
```research-result(that exact info-string) and closed with```. - Body is a single valid JSON object, the schema below.
- Emit it into your reply (the dialogue), not to a file.
- Atomicity: one assistant turn.
- On revision, emit a new full block; the harness keeps the LAST one.
- Same-source: only summarize the design
formated-specsproduced. The depth is document-level (hypothesis + design body); do not run experiments.
research-result schema
{
"title": "<one-line title of the design>",
"sections": [ {"heading": "<section heading>", "body": "<design prose>"} ],
"artifacts": [ "<spec filename or figure reference>" ]
}
Sections carry the design body. Judge nothing against academic standards.
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.
- 11d ago First seen · 34 lines · 47 tokens per session scan A 4fdfb57326bd
formated-results is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (456 stars, last pushed yesterday), licensed Apache-2.0. It adds 47 tokens to every session and 311 once invoked, about $0.0002 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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