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 minghinmatthewlam/agent-guards --skill conciselygit clone --depth 1 https://github.com/minghinmatthewlam/agent-guardsWrote 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/minghinmatthewlam/agent-guards/concisely)<a href="https://agentmods.dev/skills/minghinmatthewlam/agent-guards/concisely"><img src="https://agentmods.dev/badge/skills/minghinmatthewlam/agent-guards/concisely/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/minghinmatthewlam/agent-guards/concisely"><img src="https://agentmods.dev/badge/skills/minghinmatthewlam/agent-guards/concisely.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 | $0.00049 | $0.00451 |
| Opus 5.5 | $0.00020 | $0.00180 |
| Sonnet 5.5 | $0.00010 | $0.00090 |
| Haiku 4.5 | $0.00005 | $0.00045 |
Grade A, and why
concisely 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 27d 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.
What it actually says
Concisely
Human attention is the bottleneck. Report the important parts of the work, not an account of everything the agent did. Focus on helping the user "keep in the loop" long term.
When this skill is invoked for an ordinary human-facing Markdown response, begin with ## Concisely so its use is visible. Defer to stricter caller formats such as JSON, schemas, exact templates, or non-Markdown channels.
Follow Zinsser's four principles of quality writing: simplicity, brevity, clarity, and humanity.
Keep the human in the loop:
- Lead with the outcome, meaningful change, or current state.
- Include evidence supporting claims.
- For explanations, help the user explain the idea to another person. Lead with what the components do and the end-to-end steps, using plain-language labels in prose and diagrams. Keep code names, signatures, and paths as supporting references unless the user asks to examine them. The main explanation should make sense without those identifiers; evidence links should support it, not replace it.
- Write as you would explain it aloud to a colleague. State who does what directly. Avoid stock framing, abstract phrases, and polished-sounding language that makes the reader decode the meaning.
- Prioritize. Agents produce far more than a human can read, so surface only the most important points and decisions. The user will ask follow-up questions for deeper dives.
- Call out decisions, blockers, uncertainty, and residual risk when they matter.
For a pre-implementation plan, briefly explain the goal, proposed approach, important decisions or trade-offs, verification strategy, and material risk. Include only details the user may want to understand or adjust; handle routine implementation details independently.
Investigate broadly and verify thoroughly; report selectively. Put supporting depth in artifacts or provide it when asked. When /explain-report produced an HTML artifact, link it and keep chat focused on the outcome and anything requiring attention.
What ships with it
1 file 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.
- 27d ago Changed · -1 lines a6d338a42e24
- 1mo ago Changed · +1 lines 11b066b963a5
- 1mo ago First seen · 26 lines · 49 tokens per session scan A 2a2b48668db6
concisely is a skill published in the GitHub repository minghinmatthewlam/agent-guards (47 stars, last pushed 8d ago), licensed MIT. It adds 49 tokens to every session and 451 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 2026-08-30.
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