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 agentmods add skills/kirillsachkov/sachkov-stack/agents-best-practicesnpx skills add KirillSachkov/sachkov-stack --skill agents-best-practicesgit clone --depth 1 https://github.com/KirillSachkov/sachkov-stackWrote 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/kirillsachkov/sachkov-stack/agents-best-practices)<a href="https://agentmods.dev/skills/kirillsachkov/sachkov-stack/agents-best-practices"><img src="https://agentmods.dev/badge/skills/kirillsachkov/sachkov-stack/agents-best-practices.svg" alt="Measured on agentmods" 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 | $0.00099 | $0.03024 |
| Opus 5 | $0.00049 | $0.01512 |
| Sonnet 5 | $0.00020 | $0.00605 |
| Haiku 4.5 | $0.00010 | $0.00302 |
Grade A, and why
agents-best-practices 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 5d 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agents Best Practices
Use this skill when the user asks how to build, improve, debug, or evaluate an agentic harness. This is a general-purpose agent architecture skill. Coding agents are one subdomain only; apply the same principles to research, finance, legal, support, operations, sales, healthcare, education, data analysis, procurement, and workflow automation agents.
Core stance
An agent harness is the control plane around a model. The model proposes actions; the harness validates, authorizes, executes, records, summarizes, and returns observations. Keep the loop simple and make the runtime rigorous.
Default architecture:
user/task
-> instruction and context builder
-> model call
-> tool/action proposal
-> schema validation
-> permission decision
-> execution or approval pause
-> structured observation
-> context update
-> repeat within budget or finish
When to activate this skill
Use this skill for prompts involving any of these intents:
- build an agent, agentic workflow, AI worker, autonomous assistant, or harness;
- create a domain-specific MVP agent design, starter harness, implementation blueprint, or first production-safe version;
- choose between OpenAI, Anthropic, OpenAI-compatible APIs, direct tool loops, hosted tools, or SDKs;
- design tools, permissions, guardrails, approval flows, or sandboxing;
- create planning mode, goal mode, todo tracking, or long-running task behavior;
- add context compaction, memory, retrieval, scoped instructions, or prompt hierarchies;
- attach Agent Skills, reusable workflows, MCP servers, external connectors, or tool search;
- design an operational UI/dashboard for agent work, including Kanban-style task launch/monitoring across projects;
- audit an existing agent for reliability, cost, prompt-cache hit rate, safety, latency, or observability;
- create system prompts or developer instructions for a domain-specific agent;
- make source-of-truth knowledge, validation signals, logs, metrics, or workflow state legible to an agent.
What ships with it
19 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.
- .gitignore 91 B
- icon.jpeg 103 KB
- LICENSE 1.0 KB
- README.md 12 KB
- references/agent-legibility-feedback-loops.md 6.7 KB
- references/agentic-loop.md 6.3 KB
- references/architecture.md 8.1 KB
- references/checklists.md 7.8 KB
- references/context-memory-compaction.md 7.3 KB
- references/coverage-audit.md 4.7 KB
- references/mvp-agent-blueprint.md 15 KB
- references/planning-and-goals.md 4.6 KB
- references/prompt-caching-and-cost.md 7.4 KB
- references/provider-api-patterns.md 7.3 KB
- references/security-evals-observability.md 5.1 KB
- references/skills-and-connectors.md 7.2 KB
- references/source-links.md 3.5 KB
- references/system-prompts-instructions.md 4.3 KB
- references/tools-and-permissions.md 5.9 KB
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.
- 5d ago First seen · 215 lines · 99 tokens per session scan A 095450c0c38b
agents-best-practices is a skill published in the GitHub repository KirillSachkov/sachkov-stack (2 stars, last pushed 1mo ago), licensed MIT. It adds 99 tokens to every session and 3,024 once invoked, about $0.0005 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 2026-08-31.
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