Grounding in the code
the agent reads and searches the relevant code before its first edit.
Use caseEngineering & Product
Every agent ships work your reviewers can trust.
One bug-fixing method across every AI tool. Fewer bounced PRs and repeat review comments. You can see who has the playbook, which agents are on the current policy, and whether rework is going down.
The problem
Your team uses Claude Code, Codex and Cursor, and everyone uses them differently. A few engineers' agent PRs almost never bounce. Everyone else's come back for the same misses, and your standards live in one person's config file.
Example power-user workflow
Repro-first bug fixing
When a reviewer corrects it, she turns the comment into a rule her agent follows from then on.
Who it serves
Engineering managers and tech leads who own review standards, agent policy and AI spend.
Read the code
Study the code around the bug before editing.
Reproduce the bug
Write a failing test, then make a small, scoped fix.
Check CI failures
Read the failure and fix its cause.
Ship verified work
Include the failing-then-passing test with the PR.
Learning from sessions
Example signals to surface
Illustrative signals. Availability varies by connected agent and workflow.
Test-first reproduction
a failing test is written before any source file changes.
Checks after the last edit
tests run after the final change, not before it.
Small, scoped diffs
the change stays within the files the task needs.
Recovering from CI failures
failures are diagnosed and fixed, not blindly re-run.
Review comments that become rules
the same comment never has to be made twice.
Fixes that stick
accepted work, not reworked or reverted later.
How Halofy makes it repeatable
Engineering & Product
The approved setup
A playbook bundle, backed by team rules.
In the playbook
- The repro-first-fix recipe
- Her AGENTS.md rules
- The skill files
Team guardrails
- A Required policy: bug-fix PRs include a reproducing test
- A Plan before acting instruction
- An MCP allowlist for repos and CI
- Read only / Read & write agent badges
Every connected agent receives it and acknowledges the current policy version.
What they use
- power-user signals
- playbooks (bundled setup)
- skills
- instructions
- Required/Advisory policies
- MCP allowlist
- agent badges
- compliance standing
- adoption tracking
- budgets per engineer and per agent.
Outcomes
What your team can work toward
Adoption, output and time-saving figures are targets, not measured customer results.
~5% → ~90% of the team working like power-users.
Most teams start with a handful of people who've cracked AI. Halofy gets nearly everyone working their way.
Output up ~40% across the engineering team
on playbook workflows.
~35% less time on repeat workflows
for every engineer who adopts the playbook.
Make your best team's habits everyone's default.
One approved playbook. Every connected agent on the team.