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 compute-normalizationgit 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/compute-normalization)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/compute-normalization"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/compute-normalization/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/compute-normalization"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/compute-normalization.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 | $0.00014 | $0.00418 |
| Opus 5.5 | $0.00006 | $0.00167 |
| Sonnet 5.5 | $0.00003 | $0.00084 |
| Haiku 4.5 | $0.00001 | $0.00042 |
Grade A, and why
compute-normalization 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
Compute Normalization
Purpose
Analyze the performance-compute tradeoff across methods. Identify Pareto-optimal methods (best performance for a given compute budget), compute-normalized rankings, and efficiency frontiers. Essential for practical method selection under resource constraints.
Input Schema
| Field | Type | Description |
|---|---|---|
| method_scores | object[] | Array of {method, dataset, metric, score} |
| compute_costs | object[] | Array of {method, flops, gpu_hours, params, training_cost_usd} |
Output Schema
{
"pareto_frontier": [
{
"method": "string",
"score": 0.0,
"compute_metric": "string",
"compute_value": 0.0,
"is_pareto_optimal": true
}
],
"efficiency_rankings": [
{
"method": "string",
"score_per_flop": 0.0,
"score_per_gpu_hour": 0.0,
"score_per_param": 0.0
}
],
"compute_normalized_scores": [
{
"method": "string",
"raw_score": 0.0,
"normalized_score": 0.0,
"normalization_method": "string"
}
],
"practical_recommendations": {
"budget_low": {"method": "string", "score": 0.0, "cost": "string"},
"budget_medium": {"method": "string", "score": 0.0, "cost": "string"},
"budget_high": {"method": "string", "score": 0.0, "cost": "string"}
}
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 59 lines · 14 tokens per session scan A 9005caba36e4
compute-normalization is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (510 stars, last pushed 14d ago), licensed Apache-2.0. It adds 14 tokens to every session and 418 once invoked, about $0.0001 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 2026-09-03.
compute-normalization compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| compute-normalizationyogsoth-ai/de-anthropocentric-research-engine | Skill | 510 | 14d ago | Apache-2.0 | A | 14 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |