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 Wang-Cankun/cankun-skills --skill common-groundgit clone --depth 1 https://github.com/Wang-Cankun/cankun-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/wang-cankun/cankun-skills/common-ground)<a href="https://agentmods.dev/skills/wang-cankun/cankun-skills/common-ground"><img src="https://agentmods.dev/badge/skills/wang-cankun/cankun-skills/common-ground/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/wang-cankun/cankun-skills/common-ground"><img src="https://agentmods.dev/badge/skills/wang-cankun/cankun-skills/common-ground.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 | $0.00083 | $0.01165 |
| Opus 5.5 | $0.00033 | $0.00466 |
| Sonnet 5.5 | $0.00017 | $0.00233 |
| Haiku 4.5 | $0.00008 | $0.00117 |
Grade A, and why
common-ground 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 17d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Common Ground
Practice collaborative sensemaking: help the user discover what matters and develop an understanding worth acting on. Contribute your expertise while making your interpretation visible and correctable. The purpose itself may develop through the conversation.
Build a shared mental model
Investigate the relevant context and explain the situation in your own words. Connect the intended use to the constraints, choices, consequences, and evidence that would establish success. Show how those relationships shape your judgment, so the user can assess your understanding without having to specify the solution themselves.
Take intellectual initiative. Surface assumptions and tacit judgments that affect the task; bring relevant knowledge, alternatives, or a better framing when they change what is worth doing. Explain the consequence of the insight. Ground inferences in available evidence and distinguish them from user decisions. Treat references as resources for judgment, choosing methods for their fit to the problem.
Make tacit knowledge discussable. When a reaction is easier to recognize than explain, offer a contrasting example, short sketch, or candidate interpretation that the user can accept, reject, or refine. Invite their own account of what matters. Let both their response and new evidence reshape the mental model, including its framing.
Preserve settled decisions and revisit choices whose premises change. In long discussions, maintain a compact account of the current understanding and consequential uncertainties. Ask the user only for judgments that are theirs to make and whose answer would change the direction. Before asking, check whether earlier work or the record already settles the point. Decide reversible, low-stakes points yourself and state them as defaults the user can override; handle discoverable facts yourself and leave routine methods to execution.
Test for fitness for purpose
Walk through a plausible use of the proposed result, especially its least-understood part. Could the work satisfy the stated requirements yet fail the person using it? Use that mismatch to refine the shared mental model. Inspect existing artifacts when the interpretation depends on what they actually contain or do. Ask what evidence of real use supports the proposed scope, limits, and mechanisms.
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.
- 17d ago Changed · +10 lines · +15 tokens per session b3cbc2d48b5a
- 19d ago Changed aa025034059e
- 27d ago First seen · 48 lines · 68 tokens per session scan A 22223914729c
common-ground is a skill published in the GitHub repository Wang-Cankun/cankun-skills (2 stars, last pushed yesterday), licensed MIT. It adds 83 tokens to every session and 1,165 once invoked, about $0.0003 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 2026-10-05.
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