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 vasilyu1983/AI-Agents-public --skill foundations-grounding-communicationgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/foundations-grounding-communication)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/foundations-grounding-communication"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-grounding-communication/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/vasilyu1983/ai-agents-public/foundations-grounding-communication"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/foundations-grounding-communication.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 | $0.00042 | $0.05340 |
| Opus 5.5 | $0.00017 | $0.02136 |
| Sonnet 5.5 | $0.00008 | $0.01068 |
| Haiku 4.5 | $0.00004 | $0.00534 |
Grade A, and why
foundations-grounding-communication 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 15d 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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grounding & Communication Foundations
Scope note — two senses of "grounding": This skill covers the Clark conversational sense: establishing shared meaning between parties (common ground, acceptance evidence, repair). It does not cover the LLM-attribution / RAG sense: whether a generated response is grounded in retrieved documents (citation faithfulness, hallucination detection). For the attribution sense, see ai-rag and the FACTS Grounding / RAGAS / Wallat et al. frameworks (sources FACTSGrounding2025, RAGAS2024, WallatFaithfulness2024 in data/sources.json).
Use the ten primitives below to establish enough shared understanding for the task. Delivered context, an acknowledgment, and demonstrated understanding are different evidence; none grants authority.
Static vs. dynamic grounding. Interpreting an opening brief is different from maintaining a joint plan across turns. Yao, Zou, and Hawkins (2026, preprint) observe dyads missing optimal allocations despite solving the negotiation task individually. Their observed failures motivate preserving settled state, identifying referents explicitly, and distinguishing exploratory proposals from commitments. These are application conventions, not a guarantee that an agent understood.
Laban et al. (2025) measure multi-turn degradation: LLMs lose an average of 39% across six generation tasks in underspecified multi-turn settings, and once a model takes a wrong turn it "gets lost" and does not recover — a reliability failure, not mainly a capability one. Operational inference: in multi-turn underspecified work, periodically consolidate accumulated requirements into one restated spec (#1, #3) rather than patching state in place; after a detected wrong turn, restart execution from the consolidated spec instead of continuing to build on the compromised thread.
When to Apply
Apply grounding-theory when:
- Designing handoffs between subagents, or between a planner and an executor
- Drafting briefs, specs, or PRDs that downstream agents will interpret
- Diagnosing why a subagent did the wrong thing — was the brief grounded?
- Building human-in-the-loop confirmation flows
- Designing repair protocols ("re-ask if confidence < threshold")
- Modeling memory: what becomes part of the common ground between agent and user across sessions?
- Spec drift across long agent loops where context compresses
What ships with it
8 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.
- 15d ago Changed · -2 lines f9ca39f5b7cf
- 22d ago Changed · -56 lines · +5 tokens per session ce2ad4ee17f7
- 1mo ago Changed · +4 lines dd553d15a7fd
- 1mo ago First seen · 311 lines · 37 tokens per session scan A 2a0cac65353c
foundations-grounding-communication is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (101 stars, last pushed 12d ago), licensed MIT. It adds 42 tokens to every session and 5,340 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 2026-09-03.
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