gemini

Overview of gemini skill

What gemini is for

The gemini skill is a task wrapper for using Gemini CLI when a normal prompt is not enough, especially for code review, plan review, and very large-context analysis. Its real job is to help an agent decide when to offload work to Gemini, choose an appropriate model, and run the task without getting stuck in unattended shells.

Best-fit users and teams

This gemini skill is best for users who need one of these outcomes:

• review many files together rather than one snippet at a time
• inspect architecture plans or technical proposals end to end
• analyze very large repositories or documentation sets
• run Gemini from an agent workflow instead of manually driving the CLI

If your task is small, local, and easily handled in the current chat, this skill is usually overkill.

What makes this gemini skill different

The main differentiator is not “Gemini access” by itself. It is the operational guidance around Gemini CLI:

• when Gemini is the right tool
• how to choose a model before running
• how to avoid hangs in background execution
• how to frame a review so the output is useful instead of broad and noisy

That matters more than the wrapper name, because the biggest adoption failure here is not installation—it is launching Gemini in the wrong mode and waiting forever.

Real job-to-be-done

Use gemini when you need a second model to digest a lot of context and return a structured review, risk list, or technical assessment. The best use cases are:

• gemini for Code Review across multiple files
• plan and architecture review
• large-context repository understanding
• cross-file pattern detection and issue surfacing

Key decision before installing

Install this gemini skill if you already want Gemini CLI in your workflow and need safer, more repeatable invocation guidance. Skip it if you only need generic AI prompting or if your team is not prepared to set up Gemini CLI and authentication outside the skill itself.

How to Use gemini skill

Install the gemini skill

Add the skill from the toolkit repository:

npx skills add softaworks/agent-toolkit --skill gemini

This installs the skill definition, not the Gemini CLI binary itself. You should also have a working Gemini CLI environment available on the machine where the agent runs.

Confirm prerequisites before first run

Before relying on this gemini install in automation, check:

• Gemini CLI is installed and callable as gemini
• the CLI is authenticated
• your shell environment allows external process execution
• you know whether the run is interactive or backgrounded

The skill’s most important operational rule is about execution mode, not model quality.

Read these files first

For this skill, the fastest path is:

1. skills/gemini/SKILL.md
2. skills/gemini/README.md

SKILL.md gives the actual usage rules. README.md helps with fit and intended scenarios. There are no support folders here doing hidden work, so most of the value is in the documented workflow.

Know the non-interactive shell warning

This is the biggest practical blocker for gemini usage.

Do not run Gemini in a background or non-interactive shell with:

--approval-mode default

That mode can hang indefinitely waiting for approvals that cannot be provided.

For unattended execution, prefer:

--approval-mode yolo

And if the environment is brittle, add a timeout wrapper as the skill suggests.

Choose the model before running

The skill explicitly expects model choice up front rather than burying it in the command later. That matters because “Gemini” is not one fixed behavior. Ask which model the user wants when the task begins, especially if they care about speed, cost, or highest-quality reasoning.

If the user has no preference, frame the choice around the task:

• deep code review or plan review: choose the strongest reasoning model
• lightweight checks or iteration: choose a faster model
• very large-context analysis: favor the model intended for big inputs

Use gemini for the right task shape

The gemini skill works best when the task has all three traits:

• enough context to justify a separate CLI run
• a review or analysis objective
• a clear output format

Good requests:

• “Review this PR for correctness, maintainability, and migration risk.”
• “Analyze this architecture plan for hidden failure modes.”
• “Read this service folder and identify coupling and test gaps.”

Weaker requests:

• “Look around and tell me what you think.”
• “Review the code” with no scope, criteria, or target files

Turn a rough request into a strong gemini prompt

A rough goal like:

review this repository

should be upgraded into something like:

Use gemini for Code Review on src/payments, api/routes, and db/migrations. Focus on correctness, security, rollback risk, and missing tests. Call out only high-confidence issues. Return findings grouped by severity with file paths and short fix suggestions.

That stronger prompt improves output because it gives Gemini:

• scope boundaries
• review criteria
• output format
• confidence expectations

Provide the minimum useful input set

For high-signal gemini usage, include:

• target files, directories, PR diff, or commit range
• task type: code review, plan review, big-context analysis
• what “good” means: security, performance, architecture, testability
• desired output: bullets, table, severity tiers, fix list
• any constraints: no code changes, no speculation, cite file paths

Without this, Gemini often returns a broad essay instead of a decision-ready review.

Suggested workflow for gemini for Code Review

A practical workflow is:

1. define the review scope
2. choose the model
3. decide interactive vs background execution
4. run Gemini on the selected files or diff
5. inspect findings for specificity and false positives
6. rerun with narrower scope or stronger criteria if needed

For large repos, do not start with “review everything.” Start with the changed paths, critical modules, or the architecture boundary you actually care about.

Example prompt patterns that work better

For code review:

Use gemini for Code Review on the files changed in this branch. Focus on correctness bugs, unsafe assumptions, and missing tests. Ignore style nits. For each issue, include severity, file path, and why it matters.

For plan review:

Use gemini to review this implementation plan. Look for unclear ownership, migration risk, operational blind spots, and rollback problems. Return a short go/no-go assessment first, then detailed concerns.

For big-context analysis:

Use gemini to analyze this service across multiple folders. Identify the main data flow, cross-module dependencies, and likely failure points. Keep the answer evidence-based and cite file paths.

Practical gemini usage tips that change output quality

Small prompt changes make a big difference:

• ask for “high-confidence findings only” to reduce noise
• ask for “cite file paths” to improve trust and triage
• ask to “ignore style issues” if you want substance
• cap the scope when the first run is too broad
• specify “group by severity” if you need action prioritization

The gemini guide in this skill is most valuable when you treat Gemini like a targeted reviewer, not a general commentator.

gemini skill FAQ

Is this gemini skill only for explicit Gemini requests?

No, but explicit user intent is the clearest trigger. It also fits when the task naturally needs Gemini CLI because of large context, multi-file reasoning, or a heavyweight review. If the user simply wants a quick answer in-chat, activating gemini may add unnecessary overhead.

Is gemini good for ordinary small prompts?

Usually not. For a short code snippet or a simple explanation, standard prompting is faster and simpler. The gemini skill pays off when the task is large enough that model selection, CLI execution, and workflow discipline actually matter.

What is the biggest adoption risk?

The top risk is hanging the process in non-interactive execution by using the wrong approval mode. If you plan to automate gemini usage, understand that warning before anything else.

Is this gemini install beginner-friendly?

Moderately. The skill itself is simple, but beginners still need to understand:

• how Gemini CLI is installed outside the skill
• how authentication works in their environment
• the difference between interactive and unattended runs
• how to define a scoped review request

If those pieces are unfamiliar, expect a short setup phase.

How is this different from just writing “use Gemini”?

The gemini skill adds decision support and safer operating guidance. A plain prompt may tell an agent to use Gemini, but it may not push the user to choose a model, avoid bad approval modes, or structure the request for a review-quality result.

When should I not use gemini?

Skip gemini when:

• the task is small and local
• you do not have Gemini CLI ready
• you need a fast answer more than a deep review
• your environment cannot safely run external CLI tools
• you do not have enough scope or criteria to define the review well

Does this skill replace repository-specific review rules?

No. The gemini skill helps you invoke Gemini well, but it does not know your team’s coding standards, domain constraints, or deployment risks unless you provide them. The better your repo-specific context, the better the review.

How to Improve gemini skill

Give gemini narrower, decision-ready scopes

The fastest way to improve gemini output is to stop asking for global review unless you truly need it. Better scopes include:

• one feature area
• one PR or diff
• one architecture document
• one failure domain such as auth, billing, or migrations

Narrow scope increases specificity and reduces filler.

State the review lens explicitly

Many weak gemini results come from vague goals. Add the lens:

• correctness
• security
• migration safety
• performance regressions
• test coverage gaps
• architectural clarity

Gemini reviews become much more actionable when it knows what kind of risk to hunt for.

Demand evidence in the output

Ask gemini to include:

• file paths
• function or module names
• quoted assumptions
• why the issue matters
• confidence level if appropriate

This makes it easier to verify findings and separate real problems from plausible-sounding guesses.

Reduce false positives with better instructions

If the first pass is noisy, tighten the prompt:

• “Only include high-confidence issues”
• “Do not speculate about missing code not shown”
• “Ignore formatting and minor style concerns”
• “Prioritize defects over refactor suggestions”

That usually improves gemini for Code Review more than changing models immediately.

Iterate after the first run instead of accepting a broad answer

Treat the first output as a triage pass. Then rerun with one of these refinements:

• ask Gemini to validate the top 3 findings only
• focus on one severity tier
• inspect one subsystem in more depth
• request concrete remediation steps for accepted issues

This second pass is often where the gemini skill becomes genuinely useful rather than merely impressive.

Match execution mode to the workflow

If you improve only one operational habit, improve this one:

• interactive terminal: approval prompts may be acceptable
• agent/background mode: use unattended-safe settings and timeouts

Many “Gemini is broken” reports are really execution-mode mistakes.

Add repository context that Gemini cannot infer

Gemini can read a lot, but it still cannot infer your internal rules unless you provide them. Useful context includes:

• critical business invariants
• risky migration constraints
• security-sensitive modules
• performance budgets
• deprecated patterns to ignore or flag

This turns a generic gemini guide into a repo-aware review workflow.

Use output formatting that matches the next step

Prompt for the format you need next, such as:

• severity-grouped findings for triage
• checklist for implementation review
• go/no-go summary for plan approval
• patch suggestions for quick fixes

Better output shape reduces rework after Gemini finishes.

Watch for common failure modes

Common gemini skill failure modes include:

• prompt too broad, answer too generic
• no file scope, so findings are unfocused
• no criteria, so output mixes nits with real defects
• non-interactive hang from wrong approval mode
• missing CLI setup mistaken for skill failure

Checking these first solves most practical usage issues.

Improve gemini by improving the request, not just the model

When results disappoint, users often jump straight to model changes. In practice, gemini usage improves more from better task framing:

• clearer scope
• stronger review criteria
• required evidence
• explicit exclusions
• actionable output format

That is the highest-leverage way to get more value from this gemini skill.