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 Kshitijpalsinghtomar/depth-skills --skill ds-boundary-detectorgit clone --depth 1 https://github.com/Kshitijpalsinghtomar/depth-skillsWrote 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/kshitijpalsinghtomar/depth-skills/ds-boundary-detector)<a href="https://agentmods.dev/skills/kshitijpalsinghtomar/depth-skills/ds-boundary-detector"><img src="https://agentmods.dev/badge/skills/kshitijpalsinghtomar/depth-skills/ds-boundary-detector/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/kshitijpalsinghtomar/depth-skills/ds-boundary-detector"><img src="https://agentmods.dev/badge/skills/kshitijpalsinghtomar/depth-skills/ds-boundary-detector.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 | $0.00051 | $0.04252 |
| Opus 5.5 | $0.00020 | $0.01701 |
| Sonnet 5.5 | $0.00010 | $0.00850 |
| Haiku 4.5 | $0.00005 | $0.00425 |
Grade A, and why
boundary-detector 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 14d 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 — 473 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BOUNDARY-DETECTOR — Knowledge Boundary Probe (Premium)
Mathematical Compliance: This skill implements the DEPTH-MATHEMATICS specification. It computes and outputs ADS phase activation, CEI components, SI components, cognitive traces (knowledge gap map, void detection report), and verifies Gates V1, V3. It provides the critical "knowledge boundary" signal for the Depth .
Research Basis: Liu et al. (2023) "Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge" (arXiv:2311.09731) — UnknownBench shows most LLMs fail to consistently refuse or express uncertainty towards questions outside their parametric knowledge, although instruction fine-tuning provides marginal enhancements.
Your model has parametric knowledge — a vast but finite training corpus. Outside that boundary lies the void: questions the model has never seen, concepts that don't exist, premises that are false. The model doesn't know where the boundary is. It generates confident answers in the void.
This skill probes the boundary before other skills activate. It runs diagnostic probes to detect:
- Non-existent concepts — things that sound real but aren't in training data
- False premises — premises that contradict established knowledge
- Retrieval gaps — where external knowledge is needed but not accessed
- Ensemble disagreement — where different reasoning paths diverge
- Self-consistency failures — where the model contradicts itself on the same question
If the boundary probe detects a void, other skills MUST NOT activate on that void. They must flag, defer, or redesign.
The Failure Mode You Must Recognize
You are about to run deep-think, adversary, diverge, etc. on a question that:
- Contains concepts not in your training data (but you don't know that)
- Assumes false premises (but you pattern-matched them as familiar)
- Requires knowledge you don't have (but you'll hallucinate anyway)
Your skills will produce beautiful artifacts on empty ground. The activation heatmap will show depth. The assumption list will be thorough. The adversary will mount sophisticated attacks. But the foundation is void.
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.
- 14d ago First seen · 473 lines · 51 tokens per session scan A bbba4c2e6e28
boundary-detector is a skill published in the GitHub repository Kshitijpalsinghtomar/depth-skills (5 stars, last pushed 3d ago), licensed MIT. It adds 51 tokens to every session and 4,252 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 2026-10-03.
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