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/deephaven/deephaven-mcp/cli-help-improvenpx skills add deephaven/deephaven-mcp --skill cli-help-improvegit clone --depth 1 https://github.com/deephaven/deephaven-mcpWhat 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 | $0.00072 | $0.00793 |
| Opus 5 | $0.00036 | $0.00396 |
| Sonnet 5 | $0.00014 | $0.00159 |
| Haiku 4.5 | $0.00007 | $0.00079 |
Grade A, and why
cli-help-improve 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 — 33 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to use this vs. cli-help-accuracy: use cli-help-improve for a full review (accuracy + missing sections + output single-sourcing + reconciling all three surfaces). Use cli-help-accuracy for surgical correctness-only fixes when the help structure is already complete.
Before doing anything
Load ref-cli-help-standards — it is the contract this skill enforces. Identify the target: a single command module under cli/_commands/, or the whole CLI. For each command, the surfaced help (the command's HelpSpec, every click.option(help=...), group docstrings) is in scope; internal docstrings are not (those are pydocs-improve's job).
Steps
For each command in scope:
- Read the handler. Determine what the command actually does, every positional argument and option, every
CliError(code=...)it raises (directly or via a shared helper likeacquire_daemon), the exit codes it can return, and the exact shape of what it prints (format_output(...)payload). - Check section coverage against
ref-cli-help-standardsHelp-content contract. Add any missing section via the command'sHelpSpec: Summary, Description (with side effects), Arguments, Output, Examples (one human + one agent), See also, Exit codes, Error codes. Drop a section only when genuinely not applicable (no positional args → no Arguments; pure discovery command → no Error codes). - Single-source the output (Output is single-source). Define one
OutputSpecconstant; pass it as the spec'soutput=field (output_specderives from it). EachOutputField(name, type, help)must name a real key in the printed payload. Free-form output usesmode="text"with empty fields. - Apply
cli-help-accuracyto the command — every documented flag, argument, error code, exit code, and output field must match the code. - Strip RST (Plain text, not reStructuredText) from every surfaced string: no double-backtick literals, no
:func:/:class:roles. Use single quotes for inline literals. - Reconcile the third surface. Update the command's entry in
docs/CLI.mdso flags, arguments, exit codes, error codes, and output fields agree.docs/CLI.mdhas no automated check — verify by hand (applydocs-accuracyfor that file). - Verify per the standard's Verify section: render
--help, inspect the agents node (params + error codes + output, no double-backtick literals or\b), and run the contract tests.
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 · 33 lines · 72 tokens per session scan A 14cc6b02448b
cli-help-improve is a skill published in the GitHub repository deephaven/deephaven-mcp (5 stars, last pushed 5d ago), licensed Apache-2.0. It adds 72 tokens to every session and 793 once invoked, about $0.0004 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…