Agent harness for codebases. Gives Claude Code, Codex, and CI a shared task system, verdict ledger, and state store so agent work is traceable and auditable.
# Add to your Claude Code skills
git clone https://github.com/ReinaMacCredy/maestroLast scanned: 5/30/2026
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}maestro is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by ReinaMacCredy. Agent harness for codebases. Gives Claude Code, Codex, and CI a shared task system, verdict ledger, and state store so agent work is traceable and auditable. It has 184 GitHub stars.
Yes. maestro passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/ReinaMacCredy/maestro" and add it to your Claude Code skills directory (see the Installation section above).
maestro is primarily written in Rust. It is open-source under ReinaMacCredy on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh maestro against similar tools.
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Local-first harness for agent-built codebases. Humans steer, agents execute, maestro is the substrate.
maestro is a single Rust binary that gives a coding agent a durable place to work. Every
unit of work, what is being built, who is doing it, and the proof it was done, lives as
plain files under .maestro/ in your repo. No daemon, no hidden service state, no cloud.
The agent runs the lifecycle through the CLI; you review the artifacts.
Coding agents are fast but forgetful. They lose the thread across sessions, ship work that was never verified, and leave no trail you can audit. maestro fixes that by making the work itself durable and gated:
proposed -> ready -> in_progress -> shipped.Everything is repo-local and reviewable in a diff.
Features, tasks, and harness improvements each walk an explicit, gated state machine. The agent drives the transitions through the CLI; the gates are enforced, not advisory.
A feature carries the product contract:
stateDiagram-v2
[*] --> proposed: feature new
proposed --> ready: accept
ready --> in_progress: start
in_progress --> shipped: ship
proposed --> cancelled: cancel
ready --> cancelled: cancel
in_progress --> cancelled: cancel
shipped --> [*]: archive
cancelled --> [*]: archive
accept is gated on a frozen contract plus a behavior baseline. ship is gated on no live
child tasks plus QA coverage. amend grows a frozen contract additively with an audit reason.
A task is a unit of work, gated by proof:
stateDiagram-v2
[*] --> draft: create
note right of draft : created directly, under a feature, or spawned by harness apply
draft --> in_progress: claim
in_progress --> needs_verification: complete
needs_verification --> verified: verify
in_progress --> abandoned: abandon
needs_verification --> rejected: reject
verified --> [*]: archive
The task is the shared unit of work: a feature spins off child tasks (task create --feature)
and the harness spins off a standalone task (harness apply), both landing here in draft.
claim fast-tracks the internal accept steps when the feature contract or task checks are
present (a standalone task needs at least one --check first). verify is the evidence gate:
it passes only when the claim is backed by recorded proof.
A harness improvement is a self-improvement proposal the harness surfaces from the run log and task history; it walks its own lifecycle behind features and tasks:
stateDiagram-v2
[*] --> proposed: detector
proposed --> accepted: apply
accepted --> measured: measure (friction gone)
accepted --> proposed: measure (ineffective)
measured --> proposed: regressed
apply accepts a proposal and spawns a linked task, so the fix runs through the task lifecycle
above. measure re-runs the originating detector to close the loop: it reaches measured only
when the friction is actually gone, reverts to proposed if the fix was ineffective, and
reopens a measured item if the friction later returns. measure requires the linked task
verified unless --force.
Features and the harness are the two things that produce tasks, and both only reach their terminal state once those tasks are verified. The task lifecycle is the hub:
flowchart TB
subgraph feature [Feature]
F1[proposed] --> F2[ready] --> F3[in_progress] --> F4[shipped]
end
subgraph harness [Harness]
H1[proposed] --> H2[accepted] --> H3[measured]
end
subgraph task [Task]
T1[draft] --> T2[in_progress] --> T3[needs_verification] --> T4[verified]
end
F3 -->|"task create --feature"| T1
H2 -->|"apply spawns"| T1
T4 -->|"child tasks done + QA"| F4
T4 -->|"linked task verified"| H3
From source (always works):
git clone https://github.com/ReinaMacCredy/maestro
cd maestro
cargo install --path . --locked
With Cargo, directly from git:
cargo install --git https://github.com/ReinaMacCredy/maestro --locked
Release binary (macOS and Linux, arm64 and amd64):
curl -fsSL https://raw.githubusercontent.com/ReinaMacCredy/maestro/main/scripts/install.sh | bash
The installer drops the binary in ~/.local/bin (override with MAESTRO_INSTALL_DIR).
Verify with maestro version and maestro doctor.
maestro is meant to be driven by your coding agent. The installer wires agent skills and hooks
into your repo — including a maestro-setup skill that tunes the harness to your build/test
commands and conventions — so the agent learns the lifecycle and records its own work. Point
your agent (Claude Code, Codex, or any CLI agent) at the repo and paste:
Set up maestro in this repo: run `maestro init --yes`, then `maestro install --agent claude`
(or `--agent codex`). Then follow the maestro-setup skill it installs to tune the harness to
this repo. Start each session with `maestro status`, then drive the feature and task lifecycle
through the `maestro` CLI from there.
Scaffold the repo and install the agent integration:
maestro init --yes # create .maestro/ and extract bundled skills/hooks
maestro install --agent claude # wire skills + hooks into CLAUDE.md/AGENTS.md (or --agent codex)
maestro doctor # check the installation
maestro status # resume with the next agent action
The smallest loop is a single task. A standalone task (no feature) carries its own acceptance check, and closes once a recorded run backs its claim:
maestro task create "Patch null deref in parser" --check "regression test passes" # -> draft
maestro task explore task-001 # -> exploring
maestro task accept task-001 # locks the check -> ready
maestro task claim --next # -> in_progress
maestro task complete task-001 --summary "guard the None case" \
--claim "cargo test parser passes" --proof "observed: cargo test parser passes"
task complete records the inline proof and runs task verify automatically. If the
proof is missing or stale, the task stays in needs_verification; run
maestro query proof <id> for the repair path.
For a larger change, wrap the work in a feature contract and spin off child tasks:
maestro feature new "CSV export" # -> proposed
maestro feature set csv-export --acceptance "Export a report to CSV" --area "src/export"
# accept is gated on a captured behavior baseline. The qa-baseline skill writes this
# for you during an agent run; by hand it is just a non-empty file of [bl-NNN] scenarios:
cat > .maestro/features/csv-export/baseline.md <<'EOF'
# Behavior baseline: CSV export
## Scenario Matrix
- [bl-001] Exporting an empty report yields a header-only CSV file.
EOF
maestro feature accept csv-export # freeze the contract -> ready
cat > PLAN-csv-export.md <<'EOF'
## Task T1: Implement CSV writer
check: cargo test export passes
EOF
maestro feature prepare csv-export --from PLAN-csv-export.md
maestro task claim --next
maestro task complete task-001 --summary "wrote csv writer" \
--claim "cargo test export passes" --proof "observed: cargo test export passes"
# ship is gated on QA coverage: every [bl-NNN] baseline scenario needs a proven slice.
# The qa-slice skill writes this for you; by hand it maps each scenario to its evidence:
cat > .maestro/features/csv-export/qa-slices.yaml <<'EOF'
slices:
- scenarios: ["bl-001"]
evidence: ["cargo test export::empty_report_header_only passes"]
EOF
maestro feature ship csv-export --outcome "Shipped streaming CSV export" # -> shipped
maestro feature show <id> and maestro task show <id> render the current state and the
recorded reasoning at any point.
maestro surfaces improvement proposals once it has enough run history to spot friction, so a
fresh repo shows none (harness list -> "no improvement proposals found"). The hb-001 below
is illustrative; once the backlog has a real entry, run it through the same task loop:
maestro harness list # what friction the run log surfaced
maestro harness apply hb-001 # accept a proposal -> spawns a standalone task
maestro task set task-003 --check "deflake the integration suite" # standalone tasks need a check first
maestro task explore task-003
maestro task accept task-003
maestro task claim --next
maestro task complete task-003 --summary "stabilized the suite" \
--claim "cargo test integration passes" --proof "observed: cargo test integration passes"
maestro harness measure hb-001 # close the loop once that task is verified
harness apply spawns a standalone task, so it needs a --check before you can claim it.
harness measure will not mark the improvement measured until that linked task is verified
(pass --force to close it anyway).
The three lifecycles compose into one operating rhythm. The Quickstart above is the terse command path; this section narrates it — the agent prompt you hand off for each flow, and what the run