| name | handback-to-aifactory |
| description | From an AIFactory project, receive a correction hand-back from TFactory (sister project). When TFactory's tests found problems, this previews the prepared correction and applies it to the original spec via AIFactory's QA Fixer (writes QA_FIX_REQUEST.md + runs the fixer). The reverse of /handover-to-tfactory. |
| when_to_use | When TFactory finished testing an AIFactory feature, found failures, and you want AIFactory to fix the code. Triggers — "apply the tfactory correction", "/handback-to-aifactory", "have aifactory fix what tfactory found". |
| allowed-tools | ["mcp__aifactory__task_apply_correction","mcp__aifactory__task_status","mcp__tfactory__task_status","Bash","Read"] |
/handback-to-aifactory (AIFactory companion)
This skill lives inside an AIFactory project and is the user-facing trigger
for applying a TFactory correction to a spec. It is the mirror image of
TFactory/.claude/skills/handback-to-aifactory/. Both share the same
procedure; the only difference is which repo the slash command is typed from.
It pairs with /handover-to-tfactory to close the loop:
/handover-to-tfactory → TFactory tests → (failures) → /handback-to-aifactory
→ AIFactory QA Fixer fixes the spec → re-run TFactory to verify
Installation
From the AIFactory repo root:
mkdir -p .claude/skills/handback-to-aifactory
cp /path/to/TFactory/companion-skills/aifactory-handback-from-tfactory/SKILL.md \
.claude/skills/handback-to-aifactory/SKILL.md
The receiver this skill drives ships in AIFactory itself (issue
olafkfreund/AIFactory#317): the REST route
POST /api/tasks/{task_id}/apply-correction and the MCP tool
task_apply_correction. Make sure AIFactory's web-server is running (port
3101) and — for the MCP path — that AIFactory's own MCP server is registered in
your Claude Code MCP config so mcp__aifactory__task_apply_correction resolves.
When to use
- explicit
/handback-to-aifactory
- "apply the tfactory correction"
- "have aifactory fix what tfactory found"
Procedure
1. Get the correction payload
The correction request lives in the TFactory workspace for the task:
~/.tfactory/workspaces/<project_id>/specs/<spec_id>/findings/handback_request.md
(+ .json). If you have it, read it; if TFactory is on another host, have the
operator paste the handback_request.md contents.
2. Identify the target spec
From handback_request.json, read aifactory_task_id — it's
<project_id>:<spec_id> for the AIFactory spec to fix.
3. Preview, then apply
mcp__aifactory__task_apply_correction(
project_id=..., spec_id=...,
fix_request_md=<contents of handback_request.md>,
source=<"triage" | "visual_inspection">,
confirm=false # preview — writes/starts nothing
)
Show the operator the preview (would_write). On a clear yes, call again with
confirm=true — AIFactory writes QA_FIX_REQUEST.md onto the spec and runs the
QA Fixer.
4. Report + (optionally) re-test
Report the spec + returned status (e.g. qa_fixing). When the fixer finishes,
re-run TFactory to verify the fix: mcp__tfactory__task_rerun(task_id=...).
Failure modes
- Spec not found (404) → the
aifactory_task_id doesn't match a current
spec; check it was handed over from this project.
- MCP tool missing → register AIFactory's MCP server, or run the apply from
the TFactory side:
cd apps/backend && python -m agents.handback <spec_dir> --send.
- Fixer needs creds → the QA Fixer runs a real provider; ensure AIFactory's
LLM provider is configured.
