code-mint

Audit whether a repository's environment variable configuration allows an agent to load all required .env variables from scratch. Use when evaluating a new repository's agent-readiness, when environment loading fails during agent setup, or as part of an initial harness assessment. Do not use when .env files already load correctly and the goal is to add a single new variable, or when creating env loading capability (use autonomy-env-creator).

Creates the capacity for an agent to load all environment variables from scratch, based on the output of autonomy-env-auditor. Use when an env audit report exists and environment loading needs to be remediated. Do not use when no audit report exists (run autonomy-env-auditor first) or when debugging a single missing variable.

Audit whether an agent has working access to the SRE CLIs the project uses — version control host CLI, cloud provider CLI, and observability CLI. Tests each connection and reports access gaps. The steps below use GitHub CLI, AWS CLI, and Datadog Pup as a worked example; adapt for your project's actual tooling. Use when setting up a new agent environment, troubleshooting tool access, or as part of an initial harness assessment. Do not use when debugging a specific production issue (use interface-sre-agent) or when auditing general runtime readiness (use autonomy-staging-auditor).

Audit whether an AI agent can go from a clean checkout to a runnable application or local runtime equivalent, without unsafe provisioning or destructive setup. Use when evaluating development or staging-like runtime readiness, onboarding a new project, or checking whether the agent can install dependencies, start services, and perform smoke checks. Do not use when evaluating test strategy and coverage depth (use autonomy-testing-auditor) or when debugging a live production issue (use interface-sre-agent).

Builds the scripts, setup flow, and runtime documentation needed for an agent to install dependencies, start local services, boot the app, and run smoke checks from a clean checkout. Use when a runtime audit report exists and runtime readiness needs improvement. Do not use when no audit report exists (run autonomy-staging-auditor first) or when the main gap is test coverage depth (use autonomy-testing-creator).

Evaluate whether a repository's unit, integration, end-to-end, visual, contract, and CI test setup lets an AI agent self-verify changes through fast, isolated, and well-targeted automated feedback. Use when auditing test readiness, onboarding a new project, or assessing whether the agent can safely validate its own work. Do not use when the main question is app startup or runtime bootstrapping (use autonomy-staging-auditor) or when debugging one failing test (use interface-sre-agent).

Builds the test setup, test utilities, and high-priority coverage needed for an agent to self-verify changes across unit, integration, end-to-end, visual, contract, and CI workflows. Use when a test readiness audit report exists and the testing setup needs improvement. Do not use when no audit report exists (run autonomy-testing-auditor first) or when debugging one failing test (use interface-sre-agent).

Diagnose production issues by querying logs, metrics, and traces from observability, cloud, and CI/CD CLIs. Use when investigating errors, debugging production issues, checking service health, reviewing CI/CD status, checking recent deployments, reproducing a reported bug with evidence, or when the user mentions logs, metrics, monitoring dashboards, container services, CI pipelines, or production incidents. Do not use when the issue is a known feature request or cosmetic change (use interface-ticket-writer) or when auditing whether tools are accessible (use autonomy-sre-auditor).

Collaborates with a product owner, TPM, or engineer to refine vague requests into high-quality work tickets optimized for agent one-shot execution. Use when writing tickets, refining requirements, reviewing feature requests, or when a stakeholder submits a new task. Do not use when debugging an existing issue (use interface-sre-agent) or when auditing codebase readiness (use legibility-auditor).

Audits a repository's documentation coverage and agent-readiness by evaluating AGENTS.md placement, progressive disclosure quality, and UX intent documentation. Use when evaluating repository structure, auditing documentation coverage, assessing agent-readiness, or onboarding a codebase to agent-first practices. Do not use when creating or editing AGENTS.md files directly (use legibility-enhancer instead).

Collaboratively walks a human through creating high-quality AGENTS.md files throughout a codebase, implementing progressive disclosure architecture. Use when creating or updating AGENTS.md files, onboarding a repository to agent-first practices, or remediating findings from legibility-auditor. Do not use when evaluating existing documentation coverage (use legibility-auditor) or when creating agent skills (follow `docs/skill-development.md` instead).

Step-by-step playbook that transforms a repository for AI-first development. It scopes the repository, runs read-only audits, maintains an outcome-driven checklist, guides collaborative improvements, and verifies the results with evidence. Use when onboarding a new repository, when a user says "set up this repo for agents," or when starting a harness engineering transformation. Do not use when the repo is already onboarded and the goal is to run a single specific skill.