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 commands/strands-rl/strands-sglang/audit-docstringsgit clone --depth 1 https://github.com/strands-rl/strands-sglangWrote 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/commands/strands-rl/strands-sglang/audit-docstrings)<a href="https://agentmods.dev/commands/strands-rl/strands-sglang/audit-docstrings"><img src="https://agentmods.dev/badge/commands/strands-rl/strands-sglang/audit-docstrings.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 | $0.00000 | $0.01029 |
| Opus 5 | $0.00000 | $0.00515 |
| Sonnet 5 | $0.00000 | $0.00206 |
| Haiku 4.5 | $0.00000 | $0.00103 |
Grade A, and why
audit-docstrings 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit a package's docstrings and comments against the style rules in AGENTS.md, and fix what fails.
Target is $ARGUMENTS — a package or file path under src/strands_sglang/ (e.g. eval,
environments/harbor, sglang.py). If not provided, ask which one; never audit
everything at once.
Read the files. Do not grep for candidates.
A script cannot tell a # ---- Task ---- section divider from an explanatory comment, or
judge whether a private helper names a domain concept. Open each file in the target and
read it. Counting tools are for reporting scale, not for deciding.
The decisions, in the order they resolve
1. Args: is all-or-nothing. D417 rejects a partial section, so the question is
never "does this parameter need a line" but "do this function's parameters need
explaining at all". Default no.
Delete the whole section when every line restates its annotation:
client: SGLangClient # signature
client: `SGLangClient` for HTTP communication with the server. # the same fact
Keep it when a parameter's meaning isn't in its type: resolution order, ownership, what
None falls back to, units, what happens when two arguments disagree, or a name that
implies less than it does (env_hook_path: str is really a dotted path to a callable
returning an AsyncEnvFactory).
2. Returns:/Yields: earn their place against the annotation.
-> tuple[list[ToolResultContent], Literal["success", "error"]] needs no "a tuple of
(content, status)". A -> str that is really a JSON envelope does. The poorer the type,
the more the section is worth.
3. Notes: is a footnote; the body is the explanation. Delete the sentence and see
what breaks. If the function stops making sense it was body text — why this method
exists, which of two paths to take, what an empty return means. If all that's lost is a
warning, it stays a footnote: thread-safety, a teardown contract, an upstream quirk, a
deliberate divergence from a reference implementation.
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 · 88 lines · 0 tokens per session scan A 491a3d0f3e03
audit-docstrings is a command published in the GitHub repository strands-rl/strands-sglang (77 stars, last pushed 5d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,029 tokens. 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.
Other commands, from other repositories
commit
See .opencode/skills/git-commit/SKILL.md for commit format rules.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.