ai-hats

Do. Reflect. Repeat.

Compose AI agents from reusable roles, then run an automatic retrospective after every session.
One role set works for both Claude and Gemini.

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Why ai-hats?

Have you ever watched the same AI agent step on the same rakes across projects? Forgetting your conventions, skipping the planning step, falling back to the same anti-pattern. Copy-pasting CLAUDE.md doesn't scale: edits drift across projects, and a fix in one rarely makes it back to the others.

ai-hats answers this with two things:

• Roles as compositions of reusable components — traits, rules, skills, and hooks are assembled into a role once and injected into the system prompt of any provider (Gemini / Claude). A fix to one component reaches every role that includes it via ai-hats self init.
• Deep reflection after every session — a structured retrospective with a factual layer (metrics, files, commits) plus an LLM narrative that delivers verdicts on active hypotheses and votes on improvement proposals. Patterns observed across 3–5 sessions become new rules and skills, and the loop closes.

roles/dev-python ── trait-base + trait-agent + dev::python + dev::shell
                    ├── rules: git_workflow, tdd
                    ├── skills: backlog-manager, git-mastery
                    └── injection → GEMINI.md / CLAUDE.md

Names and core terms (role, session, reflect, backlog, …) — see [1].

Quick start

A bash launcher in ~/.local/bin/ai-hats (one-time per host) plus a per-project venv in <ai_hats_dir>/.venv/. Get help for any command with ai-hats --help. View the full CLI tree with ai-hats --tree.

ai-hats is a host tool: it is driven by that launcher (which exec's python -m ai_hats), never installed as a dependency of your project's own venv. There is no <venv>/bin/ai-hats console script — the only bin/ai-hats is the host launcher. If self update ever can't repair a broken install in-band, recover out-of-band with curl -LsSf https://github.com/muratovv/ai-hats/raw/master/scripts/bootstrap.sh | bash -s -- --repair (see [3] §10).

Prerequisite: uv is the single host requirement — the env engine that also provisions Python (no separate Python install). The one-command install below auto-installs uv if it is absent; the step-by-step path assumes it is present (curl -LsSf https://astral.sh/uv/install.sh | sh).

One command (recommended)

curl -LsSf https://github.com/muratovv/ai-hats/raw/master/scripts/bootstrap.sh | bash -s -- -r <role> -p <provider>

On a fresh host this installs the launcher, auto-installs uv if absent, creates the venv, and initializes the project — nothing pre-installed required.

Zero-install first touch

Already have uv? Wire a single project with the latest published ai-hats, no host launcher required:

uvx ai-hats self init                          # runs the stable PyPI release ephemerally

uvx fetches and runs ai-hats from the stable channel (PyPI) in a throwaway environment — handy to try it or bootstrap one project. For day-to-day use, install the launcher below so ai-hats is a persistent command on $PATH.

1. Install the launcher (once per host)

Requires uv on the host (see prerequisite above). The launcher heals/creates venvs via uv and fails loud with the install one-liner if uv is missing.

curl -sSL https://github.com/muratovv/ai-hats/raw/master/scripts/install-launcher.sh | bash

Drops a ~30-line bash launcher into ~/.local/bin/ai-hats. If ~/.local/bin/ isn't on $PATH, the installer prompts you to add it.

2. Wire ai-hats into a project

cd ~/dev/my-project
ai-hats self init                              # interactive wizard (recommended)

ai-hats self init is the human-friendly bootstrap. It:

1. Installs the latest ai-hats from the default stable channel (a published release on PyPI). Other channels — edge (a repo branch HEAD via git+https) and local (an editable working tree) — are selectable later; see [2].
2. Asks for a provider (smart default by ~/.claude / ~/.gemini presence) and writes a minimal ai-hats.yaml.
3. Hands off to the initial-wizard LLM session, which detects your stack, recommends a base role, helps with customizations, and configures the feedback (session-retro) policy — all via ai-hats config … commands.

Scripted / CI variant — pass both flags to skip the wizard:

ai-hats self init -p claude -r go-dev --no-wizard          # writes ai-hats.yaml directly

Bootstrap-time flags that are tedious to change later (the wizard also asks about these in an opt-in "advanced setup" branch):

ai-hats self init -p claude -r go-dev --no-wizard \
  --ai-hats-dir .ai \                  # framework directory (default: .agent/ai-hats)
  --venv ~/.venvs/myproj \             # point at an existing venv instead of the managed one
  --no-manage-gitignore                # do not auto-add ai-hats entries to .gitignore

3. Use it

ai-hats                       # start a session with current settings
ai-hats --resume              # flags pass through to the provider (claude / gemini)
ai-hats config status         # health-check the composition
ai-hats self init             # rebuild the prompt after library changes
ai-hats self update           # update ai-hats and auto-bump

ai-hats self update is self-healing: if a system Python upgrade breaks the venv, it is rebuilt automatically (default venvs only; override venvs are user-owned).

Full configuration walkthrough (wizard, role pick, customization, feedback policy, venv) → [2].

Alternative install paths (bash bootstrap from a clone, override venv, developing ai-hats itself) live in [3].

CLI

The full command reference with descriptions and options — ai-hats --tree (equivalent to ai-hats --help --tree).

Subtrees: ai-hats --tree <group> (e.g. ai-hats --tree wt) or deeper: ai-hats --tree task hyp.

Eight top-level groups:

Group	What it does
agent	Run a role as a sub-agent inside an isolated worktree
config	Read / edit ai-hats.yaml (provider, role, customizations, feedback)
list	Discovery: roles / skills / rules / traits / providers / tokens
reflect	Feedback loop — per-session vote and bulk triage of HYP / PROP
self	Tool lifecycle: init / update / clean / rollback
session	Observability: list / show / audit / retro for sessions
task	Backlog: task / hyp / proposal cards with a state machine — recipes in [4]
wt	Git worktrees: create / merge / discard / exec / env — recipes in [5] §2

Common scenarios:

# Interactive session with role injection
ai-hats                                    # current settings
ai-hats -p claude -r architect             # override provider and role
ai-hats --tag client=acme                  # custom tags in metrics.json

# Sub-agent in an isolated worktree
ai-hats agent sre --task "investigate alert XYZ"

# Lifecycle
ai-hats self init                          # interactive bootstrap (wizard) — new projects
ai-hats config set -r <role> -p <provider> # change role / provider in an existing project
ai-hats self update && ai-hats self init   # update ai-hats and rebuild the prompt
ai-hats config status                      # health-check the composition

Full reference — ai-hats --tree.

Customization

The shipped library splits into library/core/ (engine fundament) and library/usage/ (curated content). Role definitions live under library/core/roles/ and library/usage/roles/; you change behaviour by composing or replacing them rather than editing core code.

Reference for role changes — docs/how-to-extend.md:

• Worked example: add your own role — minimal recipe for a new role from scratch.
• Replacing a system role — override a built-in like session-reviewer.
• Pointing ai-hats at extra library paths — keep roles in an external repo and consume them globally.
• Custom verbs via shell aliases — ship a role + initial-injection prompt as a one-liner shell alias.

Same doc covers the override-precedence chain ([6]) for traits, rules, and skills. Documentation entry-point: docs/INDEX.md ([8]).

Advanced flows — custom pipeline steps, isolated worktrees, parallel sub-agents — live in [5].

Update notification

When the installed ai-hats SHA lags upstream master, a three-line Update banner appears under the Session summary at the end of each interactive session. It tells you the current and latest short SHAs, suggests ai-hats self update, and prints the opt-out env var on the dim third line.

The probe is non-blocking: a detached background subprocess fires at session start and caches the result for 24h under <ai_hats_dir>/.cache/update-check.json. The banner reads whatever's currently in the cache (stale-while-revalidate) — first probe results land in the next session, not the current one.

Suppress both probe and banner with AI_HATS_NO_UPDATE_CHECK=1 (useful for CI / scripted invocations). Term definitions — see [1].

Recovery from accidental change

Every destructive op inside ai-hats self update / self init (migrations, scaffold rewrites, .gitignore edits, .claude/settings.json writes, heal_* rewrites) snapshots the original content to a per-process trash session before touching disk:

$TMPDIR/ai-hats/trash-<utc-ts>-<pid>-XXXXXX/<project-relative-path>

The session also writes a MANIFEST.md next to the moved files listing every op (timestamp, kind, reason, original → trash path). Recover a single file:

ls $TMPDIR/ai-hats/                                # list all trash sessions
cat $TMPDIR/ai-hats/trash-<id>/MANIFEST.md         # see what's in this one
cp -r $TMPDIR/ai-hats/trash-<id>/<rel> ./<rel>     # restore

Sessions are NOT auto-cleaned — /tmp retention is enough in practice (macOS / Linux clean on reboot). To override the trash location, set AI_HATS_TRASH_DIR=<path>. To opt out entirely in CI / ephemeral environments, set AI_HATS_TRASH_DIR=- (hard-delete mode, no snapshots, WARN per op). On ENOSPC / read-only filesystem the destructive op aborts loudly (TrashFullError) rather than silently losing data. Term definitions — see [1].

Architecture

Roles compose from traits + rules + skills, a flat model, a task state machine, multi-provider injection. The full tour of the internal model, directory layout, skill format, and a sample config.yaml — see [7].

Session lifecycle launch → trace → finalize → retro	Reflection loop verdicts on HYP, votes on PROP	Composition flow role → traits → materialize
Backlog state machines · task / HYP / PROP lifecycles		Manual reflect-all periodic backlog triage

References

[1] — docs/glossary.md — naming source-of-truth for ai-hats core terms (role, session, reflect, backlog, …).

[2] — docs/how-to-configure.md — narrative walkthrough for first-time setup (wizard, role pick, customization, feedback policy, venv).

[3] — docs/how-to.md — ai-hats.yaml overlay recipes and alternative install paths.

[4] — docs/how-to-backlog.md — day-to-day ai-hats task / task hyp / task proposal recipes.

[5] — docs/how-to-advanced.md — advanced flows: custom pipeline steps (§1), worktree workflow (§2).

[6] — docs/how-to-extend.md — shipped library layout, override precedence, recipes for your own roles / traits / rules / skills.

[7] — docs/ARCHITECTURE.md — internal model, directory layout, skill format, sample config.yaml.

[8] — docs/INDEX.md — documentation catalog and entry-point: per-step wizard references plus the full list of how-to-*.md files with topic / when-to-read tags.