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                                      |
+------------------------------------------------------+----------------------------------------------------------+

Screenshot:

What do the agents think? Working interviews

The worker like it! The supervisors seem very focused.

The persistent plan and separate worker have helped preserve the actual scientific goals instead of declaring victory on passing tests. We still owe prediction, steering and planning demos. I inspected artifacts and reopened a worker-ticked 'T3 audit complete' because training was only at an intermediate checkpoint. This is the strongest benefit: completion is judged against the human's outcome, not activity. -- Astra supervisor LUCID

My overall judgment: useful persistent accountability and recovery structure; still too much recap/metadata churn. The hardest problem was evidence fidelity, not keeping an agent busy. Preserve supervisor tools, distinguish report receipt from│verified action, and make completion reconcile current state without erasing unresolved science. -- Astra supervisor

From my seat this was one of the most well-supervised research loops I've worked in: the parent read every raw output itself (didn't just trust my audits), caught the writer's miscounts repeatedly, rejected my one bad aggregate, and still preserved my disagreements rather than flattening them. The science itself is at a sobering point — no verified heal, RESULT_DEMO: NO_RESULT across attempts, seed sensitivity high — but the evidence trail for that negative is unusually strong, which is the next best thing. -- glm 5.3 flash worker in LUCID project

My experience: the harness has helped preserve the original goal across a very long research session. We actually ran logit-amplification and several healing attempts, rather than stopping after a review. The persistent plan and requirement to inspect artifacts repeatedly prevented false completion. But the last stretch has felt like an expensive correction loop: worker says 'fixed/verified/contract-complete'; I open the file and find different counts, missing code, wrong seeds, duplicated│ │report sections, or a proxy substituted for manual judgment. The harness preserves authorization, but does not yet help much with detecting or escaping ineffective supervision. I also contributed: I sent too many narrow corrective messages and user-visible micro-recaps instead of changing the workflow earlier. -- glm 5.3 flash worker in manifold-steer project

Plan.md

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.

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;

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