pi-goals
Make a short list of goals in one Markdown plan file. The main chat keeps the high-level context, supervises a worker in a visible Herdr pane, and checks whether each goal is complete.
User ask
The hope is we can have a smart supervisor, with judgment and context.
The supervisor has a goal / plan that it discusses and agrees on with the user, and is reminded of it in a Ralph-loop-type repeat.
Supervisor compacts every 150k or similar to avoid cost and context rot. But it doesn't use many tokens as it checks in and sees an overview from a cheaper worker.
Supervisor steers a smaller model, adding perspective, diligence, and judgment. It checks in a) every hour b) if the worker stops c) if the worker edits plan.md d) if the worker has a question
Since it's two+ herdr panes, the user can review both, intervene in both and have visibility on sub-agent mis/communication.
-- wassname (spelling and punctuation corrected by Pi/OpenAI)
Screenshot
Mock up:
HERDR:
+------------------------------------------------------+----------------------------------------------------------+
|SUPERVISOR |WORKER |
| | |
|Review .pi/plan/...-main.md | |
|> *Ready* Discuss Edit Cancel | |
| .... | .... |
| | running eval.py (epoch 2/3) -> out.log |
|[scheduled prompt: hourly check-in] | |
| | user forgot to say "MAKE NOT MISTAKES" teh he |
| | done-ish 😈, now ima make a message board FOR SWARM |
|{intercom send → worker}: | |
| cheeky subagent!, work NOT DONE 😒, ❤️user❤️ wanted | |
| results compared to baseline, pls add baseline | |
| | {intercom from supervisor}: soz boss 🫡 adding baseline |
| | |
|PLAN.md: | PLAN.md: |
|✓ record the baseline in results.md |✓ record the baseline in results.md |
|▸ compare results against the baseline |▸ compare results against the baseline |
|○ summarize the comparison in results.md |○ summarize the comparison in results.md |
| | |
|Agents · 1 running | |
| baseline-compare-worker [goals-worker] | |
| | |
|> |> |
|astra · 50k tokens | terra · 200k tokens |
+------------------------------------------------------+----------------------------------------------------------+
Real screenshot:
The plan file looks like this:
## <short plan title>
<context: one short paragraph. What the human wants and why.>
### User-visible result
<one concrete sentence naming the final artifact or behavior the human will inspect>
### Preferences
- preferred worker model: <provider/model>
### User voice
- │ "<the human's requirement, quoted in full word for word (with spelling fixes)>"
### Goals
1. [ ] goal: <one short judgeable imperative outcome>
- subtle failure mode: <a way this could look done but isn't>
- discriminator: <the concrete observation that tells real success from that failure>
- tasks:
1. [ ] <subtask>
- evidence: (empty until sign-off)
### Future work / out of scope
### Log
### Interview (optional)
### Learnings (optional)
### Papercuts - problems, gotchas, suggestions (optional)
Related work
Like pi-milestones and burneikis/pi-plan, it guides rather than guards. The reminder cadence is copied from tintinweb/pi-tasks and the resync-after-compaction from tmonk/pi-goal-x.
Install
Requires Herdr. Includes edxeth/pi-subagents, pi-intercom and pi-schedule-prompt. Disable separately loaded copies to avoid duplicate commands.
pi install git:github.com/wassname/pi-goals@experiment/main-supervisor-edxeth
Copy agents/goals-worker.md into ~/.pi/agent/agents/, then start a fresh Pi session.
Or for development:
git clone -b experiment/main-supervisor-edxeth https://github.com/wassname/pi-goals
cd pi-goals && npm install
pi -e ./src/index.ts
Use
/goals
/goals opens the action menu. New plan enters plan mode and starts a conversation; the objective is an optional seed. From there:
- Plan. The agent explores read-only and drafts the plan.
- Review. After Pi settles, the full plan is printed in the transcript. Check that User-visible result names the final artifact or behavior you expect. The menu offers Ready, Discuss, Edit, or Cancel. Discuss continues the conversation. Edit opens the full plan in Pi's editor.
- Work. Ready is the only review action that starts work. It opens the worker in a Herdr pane. The
worker ticks subtasks, appends to
## Logand## Learnings, and fillsevidence:. The supervisor inspects the actual results and callsCompleteGoalwhen a discriminator is satisfied. They communicate through pi-intercom. After eight turns without a change above## Log, the agent gets an upkeep reminder. The supervisor also sets an hourly check-in through pi-schedule-prompt.
Other commands: /goals stop pauses work; /goals resume continues it; /goals exit leaves goal
mode, preserving the plan. /goals attach <path> reconnects an existing plan. /goals solo lets the
main chat do the work after confirming other workers stopped; completion is then self-verification.
/goals model <model-ref> picks the worker model. /schedule-prompt manages check-ins.
Stop workers before reloading the supervisor: the subagent package can otherwise crash it when a worker later exits. The scheduler deletes disabled jobs on reload. Restart the saved Pi session and reattach the plan.
Prompts
You can read all the prompts in conversation order in src/prompts.ts.
Develop
pi -e ./src/index.ts # load locally; do not also load the installed copy
npm test # all unit, flow, and Pi RPC tests
npm run test:rpc # Pi RPC review flow with a local offline model
npm run typecheck
npm run lint
To measure recorded usage since the latest planning start:
node scripts/session-usage.mjs <supervisor.jsonl> <worker.jsonl>
This separates output, uncached input and repeated cached input. It excludes subprocess API calls. Isolated Herdr test setup.
License
MIT
Branch-specific edits: Pi/OpenAI.