Borrowing it
Nothing to install: this file belongs to dboeckli/ai-agent-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dboeckli/ai-agent-skills/master/.claude/skills/cc-best-practices/SKILL.mdgit clone --depth 1 https://github.com/dboeckli/ai-agent-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/dboeckli/ai-agent-skills/cc-best-practices)<a href="https://agentmods.dev/skills/dboeckli/ai-agent-skills/cc-best-practices"><img src="https://agentmods.dev/badge/skills/dboeckli/ai-agent-skills/cc-best-practices/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/dboeckli/ai-agent-skills/cc-best-practices"><img src="https://agentmods.dev/badge/skills/dboeckli/ai-agent-skills/cc-best-practices.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk 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 | $0.00100 | $0.02204 |
| Opus 5 | $0.00050 | $0.01102 |
| Sonnet 5 | $0.00020 | $0.00441 |
| Haiku 4.5 | $0.00010 | $0.00220 |
Grade A, and why
cc-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 10d 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claude Code Best Practices
Based on the official Anthropic documentation at https://code.claude.com/docs/en/best-practices.
The single most important constraint: Claude's context window fills up fast, and performance degrades as it fills. Every best practice flows from this.
Instructions
Step 1: Always give Claude a way to verify its work
Provide a runnable check (test suite, build exit code, linter, diff script) so Claude can confirm success independently. Ask for evidence (test output, command result), not just assertions.
Step 2: Use the Explore → Plan → Implement workflow for non-trivial tasks
Enter /plan mode, let Claude read the codebase first, then draft a plan before writing any code. Exit plan mode to implement. Skip this only for small, obvious changes.
Step 3: Write specific, scoped prompts
Name files (@filename), describe symptoms rather than guesses, reference existing patterns. Vague prompts produce vague results.
Step 4: Keep CLAUDE.md short and actionable
Include only what Claude cannot infer from the code. Every line should answer: "Would removing this cause Claude to make mistakes?" If not — cut it.
Step 5: Manage context aggressively
Use /clear between unrelated tasks. After two failed corrections on the same issue: clear and write a better prompt. Use /compact <hint> to compact with focus.
Step 6: Use subagents for investigation and review
Let subagents explore unfamiliar code or review your implementation — they run in a fresh context without bias toward the code they just wrote.
Examples
Example 1: Implementing a feature correctly
User says: "I keep getting flaky results when I ask Claude to implement something"
Actions:
- Add a verification step to the prompt: "write a validateEmail function — run the existing test suite after implementing, show me the output"
- If no tests exist: "write the function AND write tests for it, run them, show results"
- Set a Stop hook to block turn completion until tests pass
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.
- 10d ago First seen · 256 lines · 100 tokens per session scan A 35013f320019
cc-best-practices is a skill published in the GitHub repository dboeckli/ai-agent-skills (0 stars, last pushed 16d ago), licensed MIT. It adds 100 tokens to every session and 2,204 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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