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 bridge-validationgit 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/bridge-validation)<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/bridge-validation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/bridge-validation/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/bridge-validation"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/bridge-validation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 59 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00027 | $0.00530 |
| Opus 5 | $0.00014 | $0.00265 |
| Sonnet 5 | $0.00005 | $0.00106 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
bridge-validation 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 9d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bridge Validation
Validate analogy depth and transfer viability before committing resources to transfer.
Stages
Stage 1: Analogy Quality Assessment
Apply analogy-quality-assessment SOP to each candidate analogy. Classify depth:
- Surface: Shared object attributes only (e.g., "both are round") — REJECT
- Structural: Shared relational structure (e.g., "both use negative feedback to maintain homeostasis") — ACCEPT
- Systemic: Shared higher-order constraints and causal structure — STRONG ACCEPT
Stage 2: Transfer Adaptation
For accepted analogies, test transfer viability using transfer-adaptation SOP:
- Can the principle be stated independently of source domain?
- Does the target domain have the necessary substrate for the principle?
- What adaptations are needed to fit target constraints?
Stage 3: Structural Consistency Check
Verify the adapted transfer maintains structural consistency:
- Mapped relations preserve directionality
- Higher-order constraints are respected
- No critical source elements are unmapped without justification
- The transfer does not violate known target domain physics/logic
Minimum Yield
| Metric | Floor |
|---|---|
| Analogies assessed | all candidates |
| Validated deep analogies | ≥2 |
| Transfer viability confirmed | ≥2 |
| Structural consistency verified | ≥2 |
Available SOPs
| SOP | Role |
|---|---|
| analogy-quality-assessment | Stage 1 — classify analogy depth |
| transfer-adaptation | Stage 2 — test and adapt transfer |
| structural-mapping | Stage 3 — verify structural consistency |
| abstraction-extraction | Support — re-abstract if mapping fails |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| analogy-quality-assessment | Assess analogy depth (surface/structural/systemic). Determines whether an analogy warrants transfer investment. |
| structural-mapping | Map source→target structural correspondences. Identifies corresponding, missing, and extra elements between domains. |
| transfer-adaptation | Adapt transferred principle to target problem constraints. Produces concrete adapted solutions from abstract principles. |
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.
- 9d ago First seen · 72 lines · 27 tokens per session scan A 40efbc3809c4
bridge-validation is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (464 stars, last pushed today), licensed Apache-2.0. It adds 27 tokens to every session and 530 once invoked, about $0.0001 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 2026-09-03.
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