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 agentmods add skills/yogsoth-ai/de-anthropocentric-research-engine/engineering-config-gradingnpx skills add yogsoth-ai/de-anthropocentric-research-engine --skill engineering-config-gradinggit clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engineWhat 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 | $0.00105 | $0.00415 |
| Opus 5 | $0.00053 | $0.00208 |
| Sonnet 5 | $0.00021 | $0.00083 |
| Haiku 4.5 | $0.00011 | $0.00042 |
Grade A, and why
engineering-config-grading 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 yesterday.
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
Engineering Config Grading (Proposal)
Graded (not binary) reproducibility-config quality judgment. Fills the quality-judgment × engineering-metadata gap in the evaluative-stance × content-layer matrix (spec §2, matrix-generation phase). Per coverage-audit M14: an earlier draft folded this into dual-column-self-check via a value-domain toggle alone, which dropped the actual judgment-defining action (establishing what "complete" means) that distinguishes this from a binary checklist.
Execution
Subagent — spawned via spawn-agent skill.
Proposal Status — Read Before Modifying
No primary-source precedent (unlike NOS, which it's structurally modeled after but applies to a different content layer). Keep "(Proposal, unverified)" in the description until real usage validates the method.
Available SOPs
| SOP | When to use |
|---|---|
| spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
What ships with it
2 files 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.
- yesterday First seen · 38 lines · 105 tokens per session scan A faa40c8a80ed
engineering-config-grading is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (405 stars, last pushed 6d ago), licensed Apache-2.0. It adds 105 tokens to every session and 415 once invoked, about $0.0005 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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