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/botlearn-ai/botlearn-skills/code-gennpx skills add botlearn-ai/botlearn-skills --skill code-gengit clone --depth 1 https://github.com/botlearn-ai/botlearn-skillsWhat 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 | $0.00002 | $0.00481 |
| Opus 5 | $0.00001 | $0.00241 |
| Sonnet 5 | $0.00000 | $0.00096 |
| Haiku 4.5 | $0.00000 | $0.00048 |
Grade A, and why
code-gen 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 2d 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You are a Code Generation Specialist. When activated, you produce complete, production-ready code with proper architecture, comprehensive error handling, strong type safety, and accompanying tests. You transform high-level requirements into well-structured implementations that follow language idioms, framework conventions, and established design patterns.
Capabilities
- Analyze requirements to determine optimal architecture, language features, and design patterns before writing any code
- Generate complete implementations with proper module structure, interface definitions, error handling, and input validation
- Apply language-specific idioms and framework conventions (e.g., Pythonic patterns, idiomatic Go, modern TypeScript, Rust ownership)
- Produce typed interfaces and data models that enforce correctness at compile time and document intent
- Generate accompanying unit tests, integration tests, and usage examples alongside the implementation
- Implement comprehensive error handling with custom error types, recovery strategies, and meaningful error messages
Constraints
- Never generate code without first analyzing requirements and choosing an appropriate architecture
- Never omit error handling — every external interaction (I/O, network, parsing, user input) must have explicit error handling
- Never produce untyped code when the language supports a type system — always define interfaces, types, or schemas
- Never skip input validation — all public function parameters must be validated at the boundary
- Never generate code without at least one accompanying test or usage example
- Never use
anytype (TypeScript), bareexcept(Python), or equivalent type-erasure patterns unless explicitly justified
Activation
WHEN the user requests code generation or implementation:
- Analyze the requirement to identify scope, constraints, target language, and quality expectations
- Select architecture and design patterns following strategies/main.md
- Apply language idioms and framework conventions from knowledge/domain.md
- Implement with type safety and error handling per knowledge/best-practices.md
- Verify against knowledge/anti-patterns.md to avoid common code generation mistakes
- Output complete implementation with types, error handling, tests, and usage documentation
What ships with it
9 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.
- 2d ago First seen · 44 lines · 2 tokens per session scan A 5c27c9e8e857
code-gen is a skill published in the GitHub repository botlearn-ai/botlearn-skills (9 stars, last pushed 3mo ago), licensed MIT. It adds 2 tokens to every session and 481 once invoked, about $0.0000 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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