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name agent-ready
description Assesses a git repository's readiness for use by AI coding agents using the agentready CLI, then walks through and addresses each gap. RHDH-aware: detects RHDH repositories and uses rhdh-repos.md context to pre-fill AGENTS.md and skip inapplicable findings. Use when asked to "assess agent readiness", "run agentready", "check how agent-ready this repo is", "make this repo agent-ready", "improve agent readiness score", "assess all RHDH repos", "batch agent readiness", or "onboard this repo for agents".

Prerequisites

uvx is a hard dependency. Verify it is available before any other step:

uvx --version

If missing, stop: "uvx is required. Install via pip install uv or see uv installation."

Step 1: Mode selection

If no path was provided, present a structured choice:

  • Single repo — assess the current working directory (default)
  • Batch — assess all RHDH repositories (see Batch mode below)

If a path was provided, skip this and proceed to Step 2.

Step 2: Setup

Path: Use the provided path, or . for the current directory. Validate it is a git repository:

git -C . rev-parse --is-inside-work-tree  # replace . with path if provided

If not a git repository, stop and tell the user.

RHDH detection: Check the repo's git remote URL:

git -C <path> remote get-url origin 2>/dev/null

Attempt to read ~/.claude/skills/rhdh/references/rhdh-repos.md. If the file does not exist, skip RHDH detection and proceed with generic assessment — do not stop or warn the user. If found, check whether the remote URL matches any repo's upstream URL. If matched, note the repo name, tech stack, key paths, and conventions — these inform AGENTS.md generation and finding triage. Store as rhdh_context.

Config file: Only use a config file if the user explicitly provided one. Do not ask.

Step 3: Run the assessment

REPORT_DIR=$(mktemp -d)  # on Windows: use %TEMP% or Python tempfile
uvx --from git+https://github.com/ambient-code/agentready agentready -- assess \
  -o "$REPORT_DIR" \
  <path>

Append -c <config-path> if the user provided a config file.

Note the value of $REPORT_DIR — shell variables do not persist across tool calls.

Parse $REPORT_DIR/assessment-latest.json. Extract:

  • overall_score, certification_level
  • findings — each with attribute.id, attribute.tier, attribute.default_weight, attribute.name, status, score, evidence, remediation

Step 4: Present summary

Score: <overall_score>/100 — <certification_level>
Failing: <N> findings (<N1> Tier 1, <N2> Tier 2, ...)

If no failing findings, congratulate the user and stop.

Otherwise ask:

"Fix applicable findings automatically, or review each one individually? auto (default) — apply self-contained fixes immediately; prompt only when input is needed review — prompt yes/skip/defer/quit for every finding"

Default to auto if the user says yes, presses Enter, or says "fix everything".

Step 5: Work through findings

Work only through status == "fail" findings. Skip not_applicable and pass silently.

Sort order: ascending tier, then descending attribute.default_weight within each tier.

If rhdh_context is set, skip findings that clearly don't apply to the detected tech stack (e.g., lock file checks for a Bash-only repo, src/ layout for a GitOps YAML repo) — note them in the summary.

Auto mode

Apply each fix without prompting unless:

  • The fix requires project-specific input (CI platform, package ecosystem)
  • The finding might not apply to this repo type — present it and ask whether to apply or skip

Skip without prompting: ADRs, design intent, architecture decisions — these require human rationale. Note them in the final summary.

For the agent_instructions finding, follow the inline AGENTS.md generation in the agent_instructions section below — this applies in both auto and review modes.

After processing, list what was applied, prompted, and skipped, then proceed to Step 6.

Review mode

For each finding:

[Tier <N>] <attribute.name> — <score>/100
Evidence: <evidence items>

Remediation: <remediation.summary>

Apply this fix? [yes / skip / defer / quit]

yes — apply the fix, then move to the next finding. skip — move on; do not revisit. Use this if the finding doesn't apply to this repo. defer — note it; surface again after re-run. quit — stop immediately.

ADR and design intent findings: Do not use JSON remediation. Ask instead:

"Do you have any architectural decisions worth capturing? Describe the decision and rationale — I'll write the ADR. Skip to add manually later."

Write only if the user provides input. Never invent rationale.

agent_instructions finding (both modes)

Generate AGENTS.md and CLAUDE.md inline — do not delegate to another skill.

Scan the repo for commands:

  • package.json → scripts entries (build, test, lint, typecheck, dev)
  • Makefile / GNUmakefile → targets
  • pyproject.toml → [tool.pytest], [tool.ruff], [tool.mypy], [project.scripts]
  • .github/workflows/*.yml → run: steps containing test/lint/build/typecheck keywords

If rhdh_context is set: pull key paths, tech stack, conventions, and branching model directly from the matched rhdh-repos.md entry — use these to pre-fill AGENTS.md sections and skip generic questions where RHDH context already answers them.

If not RHDH (or RHDH context doesn't cover it): ask these three questions one at a time:

  1. "What are 2-3 conventions an agent couldn't discover by reading the code? Skip if none."
  2. "Any non-obvious architectural decisions or places where things live unexpectedly? Skip if obvious."
  3. "Any commit format, CI checks, or PR conventions agents should know? Skip if standard."

Write AGENTS.md:

# <repo-name>

## Build & Test Commands
- Build: `<command>`
- Test all: `<command>`
- Test single file: `<command>`
- Lint: `<command>`
- Type check: `<command>`

## Key Conventions
<from scan + questions/rhdh_context>

## Architecture
<from questions/rhdh_context — omit if nothing to say>

## PR Conventions
- Agent-assisted commits should include an `Assisted-by: <model>` footer
<from questions>

Write CLAUDE.md with exactly: @AGENTS.md

Omit any section — including its header — where there is nothing to say. Do not invent content.

Applying other fixes (both modes)

Use remediation.steps, remediation.commands, and remediation.examples from the JSON. Do not invent steps beyond what the JSON provides.

Step 6: Re-run and present results

REPORT_DIR=$(mktemp -d)  # on Windows: use %TEMP% or Python tempfile
uvx --from git+https://github.com/ambient-code/agentready agentready -- assess \
  -o "$REPORT_DIR" \
  <path>

Show before/after:

Before: <old_score>/100 (<old_certification_level>)
After:  <new_score>/100 (<new_certification_level>)

Remaining failures: <N> findings

If remaining failures (including deferred), ask: "Would you like to continue addressing the remaining findings?" If yes, repeat Step 5.

Batch mode

When the user selects batch assessment:

  1. Ask: "What directory are your RHDH repos cloned into? (e.g. ~/git)"
  2. Find subdirectories that are git repos:
find <dir> -maxdepth 2 -name ".git" -type d | sed 's|/.git||'
  1. For each, check if the remote URL matches a repo in rhdh-repos.md. Assess only matching repos.
  2. Run the assessment on each matched repo (Step 3) and collect results.
  3. Present a summary table:
Repo                        Score   Level              Failing
rhdh                        72/100  Bronze             4
rhdh-operator               45/100  Needs Improvement  11
rhdh-plugins                88/100  Silver             1
  1. Ask: "Would you like to address findings for any of these repos?" If yes, the user picks one — run Step 3 (assessment) on that repo to get fresh findings, then run Steps 4–6 for it.

Gotchas

  • The first uvx run fetches and builds agentready from GitHub — this can take 30–60 seconds. Subsequent runs use the cache. If the fetch fails, stop — do not proceed without a valid report.
  • Do not output the report to the repository directory — use the temp dir to avoid polluting the working tree.
  • not_applicable findings reflect the detected language stack; do not mention them unless the user asks.
  • Deferred findings surface again after the re-run.
  • Never invent rationale for ADRs or design docs. In auto mode, skip them. In review mode, ask for rationale before writing anything.
  • In batch mode, only assess repos whose remote URL matches rhdh-repos.md — do not assess unrelated repos in the same directory.
  • rhdh-repos.md is expected at ~/.claude/skills/rhdh/references/rhdh-repos.md — the default install path when using npx skills add redhat-developer/rhdh-skill. If the rhdh skill was installed to a different prefix, RHDH detection will silently degrade to generic mode. This is by design — no error, no warning.