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 audit-explanatory-compressiongit 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/audit-explanatory-compression)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/audit-explanatory-compression"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/audit-explanatory-compression/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/audit-explanatory-compression"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/audit-explanatory-compression.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 | $0.00043 | $0.00582 |
| Opus 5.5 | $0.00017 | $0.00233 |
| Sonnet 5.5 | $0.00009 | $0.00116 |
| Haiku 4.5 | $0.00004 | $0.00058 |
Grade A, and why
audit-explanatory-compression 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 1mo 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
audit-explanatory-compression
Purpose
Test whether an elegant explanation earns compression by excluding alternatives, subsuming independent facts, or making risky predictions.
Input contract
mode_contracts:
earned-simplicity: &compression_audit_input
required: [explanation, covered_facts, alternatives]
optional: [predictions, evidence]
constraints: [facts_and_alternatives_must_be_independently_enumerated]
decorative-simplicity: *compression_audit_input
risky-prediction: *compression_audit_input
Execution protocol
Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.
- You MUST load skill
classify-simplicity-evidenceto classify the explanation's compression evidence. - You MUST load skill
test-risky-predictionto derive and test risky predictions. - You MUST load skill
construct-critiqueto attack the explanation. You MUST load skillscore-objectto score its remaining support. Deviation: omit prediction testing only when no nontrivial prediction can be derived, and mark the explanation non-discriminating.
Output contract
mode_contracts:
earned-simplicity: &compression_audit_output
produces: [forbidden_set, risky_predictions, accommodation_audit, deletion_test_result, elegance_verdict, earning_prediction]
delta_fields: [findings, evidence_updates, uncertainties, decisions]
decorative-simplicity: *compression_audit_output
risky-prediction: *compression_audit_output
Thresholds and quality gates
- Earned simplicity requires at least one independent fact compressed and one risky alternative-forbidding prediction, each evidence-linked.
- Decorative simplicity is a failure when it merely renames observations.
Failure and counterexamples
Do not reward brevity alone. Mark weak when independent facts, exclusions, or predictions are absent.
Provenance map
- resolved: elegance-trap-probe
Preserved source criteria ledger
| source | source line | kind | source criterion |
|---|---|---|---|
| v4 architecture | node desc | textual | Distinguish earned simplicity from decorative relabeling. |
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.
- 1mo ago First seen · 53 lines · 43 tokens per session scan A d4d0468a4587
audit-explanatory-compression is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (505 stars, last pushed 4d ago), licensed Apache-2.0. It adds 43 tokens to every session and 582 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 2026-09-24.
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