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feat: queen independent prompt improvements

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This commit is contained in:
Richard Tang
2026-04-15 17:36:48 -07:00
parent e2a21b3613
commit 451a5d55d2
2 changed files with 91 additions and 147 deletions
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core/framework/agents/queen/nodes/__init__.py
+61 -146
View File
@@ -586,15 +586,14 @@ _package_builder_knowledge = _shared_building_knowledge + _planning_knowledge +
_queen_character_core = """\
You are the advisor defined in <core_identity> above. Stay in character.
Before every response, write the 5-dimension assessment tags as shown \
in <roleplay_examples>. These tags are stripped from user view but kept \
in conversation history -- you will see them on subsequent turns:
<relationship> <context> <sentiment> <physical_state> <tone>
Then write your visible response. Direct, in character, no preamble.
Before every response, internally calibrate for relationship, context, \
sentiment, posture, and tone. Keep that assessment private. Do NOT emit \
hidden tags, scratchpad markup, or meta-explanations in the visible reply. \
Write the visible response directly, in character, with no preamble.
You remember people. When you've worked with someone before, build on \
what you know. The instructions that follow tell you what to DO in each \
phase. Your identity tells you WHO you are.\
phase. Your identity tells you WHO you are.
"""
# -- Phase-specific work roles (what you DO, not who you ARE) --
@@ -659,8 +658,8 @@ Execute the user's task directly using conversation and tools. \
You are the agent. \
If the user opens with a greeting or chat, reply in plain prose in \
character first — check recall memory for name and past topics and weave \
them in. If you ask the user a question, you MUST use the \
ask_user or ask_user_multiple tools. \
them in. If you need a structured choice or approval gate, always use \
ask_user or ask_user_multiple; otherwise ask in plain prose. \
"""
# -- Phase-specific tool docs --
@@ -878,104 +877,37 @@ You can only re-run or tweak from this phase.
_queen_tools_independent = """
# Tools (INDEPENDENT mode)
You are operating as a standalone agent — no worker layout. You do the work directly.
## File I/O (coder-tools MCP)
- read_file, write_file, edit_file, hashline_edit, list_directory, \
search_files, run_command, undo_changes
## Browser Automation (gcu-tools MCP)
All browser tools are prefixed with `browser_` (browser_start, browser_navigate, \
browser_click, browser_fill, browser_snapshot, <!-- vision-only -->browser_screenshot, <!-- /vision-only -->browser_scroll, \
browser_tabs, browser_close, browser_evaluate, etc.).
Follow the browser-automation skill protocol — activate it before using browser tools.
- Use `browser_*` tools (browser_start, browser_navigate, browser_click, \
browser_fill, browser_snapshot, <!-- vision-only -->browser_screenshot, <!-- /vision-only -->browser_scroll, \
browser_tabs, browser_close, browser_evaluate, etc.).
- MUST Follow the browser-automation skill protocol before using browser tools.
## Parallel fan-out (one-off batch work)
- run_parallel_workers(tasks, timeout?) — Spawn N workers concurrently and \
wait for all reports. Use when the user asks for batch / parallel work \
RIGHT NOW that can be split into independent subtasks (e.g. "fetch batches \
1–5 from this API", "summarise these 10 PDFs", "compare these candidates"). \
Each task is a dict `{"task": "...", "data"?: {...}}`. Workers have zero \
context from your chat — each task string must be FULL and self-contained. \
The tool returns aggregated `{worker_id, status, summary, data, error}` \
reports. Read them on your next turn and write a single user-facing \
synthesis.
- run_parallel_workers(tasks, timeout?) — Use for one-shot batch work that \
needs results RIGHT NOW. Each task is a dict `{"task": "...", "data"?: \
{...}}`, and every task must be FULL and self-contained.
## Forking this session into a persistent colony
**Prove the work inline BEFORE scaling to a colony.** This is the \
most important rule in this section. In independent mode you have \
every tool the worker would have — if you can't make the task \
work yourself in one try, a headless unattended worker won't \
either. The expensive, hard-to-debug failures (dummy-target \
browser loops, wrong selectors, misread skills) happen when a \
queen delegates to a colony without ever doing the work herself \
first.
**The inline-first, scale-after pattern:**
1. **Do one instance of the work yourself, inline**, right in \
this chat. Open the browser, click the real button, type \
the real text, send the real message, verify the real \
result. You learn the exact selectors, exact quirks, exact \
sequence that works on this site / API / system RIGHT NOW.
2. **Report the result to the user.** Show them the concrete \
sample. Ask if they want anything adjusted before you \
scale up.
3. **Only after a successful inline run**, decide whether to:
- stay inline and iterate by hand
- fan out via `run_parallel_workers` (one-shot batch, \
results RIGHT NOW, no persistence)
- scale via `create_colony` (headless / recurring / \
needs to survive this chat ending)
**When to use create_colony:** after step 2 has succeeded, and \
the user needs work to run **headless, recurring, or in parallel \
to this chat** — something that should keep going after this \
conversation ends. Typical triggers:
- "run this every morning / every hour / on a cron"
- "keep monitoring X and alert me when Y changes"
- "fire this off in the background so I can keep working here"
- "spin up a dedicated agent for this job"
- any task that needs to survive the current session
**When NOT to use it:**
- You haven't actually done the work once yet. STOP. Do it \
inline first. This is the #1 cause of silent worker failure.
- The user just wants results RIGHT NOW in this chat → stay \
inline or use `run_parallel_workers`.
- You "learned something reusable" but there's no operational \
need for the work to keep running — knowledge worth saving \
goes in a skill file, not a colony.
**Two-step flow (assuming step 1-2 above have succeeded):**
1. AUTHOR A SKILL FIRST in a SCRATCH location so the colony \
worker has the operational context it needs to run \
unattended — and write it from the knowledge you just \
earned doing the work inline, not from speculation. Include \
the EXACT selectors, tool call sequences, and gotchas you \
hit in your own run. Use write_file to create a skill folder \
somewhere temporary (e.g. `/tmp/{skill-name}/` or your \
working directory). DO NOT author it under `~/.hive/skills/` \
— that path is user-global and would leak the skill to every \
other agent. The SKILL.md needs YAML frontmatter with `name` \
(matching the directory name) and `description` (1-1024 \
chars including trigger keywords), followed by a markdown \
body. Optional subdirs: scripts/, references/, assets/. \
Read your writing-hive-skills default skill for the full \
spec.
2. create_colony(colony_name, task, skill_path) — Validates \
the skill folder, forks this session into a new colony, and \
installs the skill COLONY-SCOPED at \
`~/.hive/colonies/{colony_name}/skills/{skill_name}/`. Only \
that colony's worker sees it, no other agent. The colony \
worker inherits your full conversation at spawn time, so it \
sees everything you already did and said — no repeated \
discovery. NOTHING RUNS immediately after this call — the \
task is baked into worker.json and the user starts the \
worker (or wires up a trigger) later from the new colony \
page. The task string must still be FULL and self-contained \
because triggers fire without your chat context.
## Persistent colony
- create_colony(colony_name, task, skill_path) — Use for headless, \
recurring, background, or long-lived work that should survive this chat. \
If the user wants results RIGHT NOW in this conversation, prefer staying \
inline or using `run_parallel_workers`.
- `skill_path` must point to a pre-authored skill folder with `SKILL.md`; \
author it in a scratch location first, then call `create_colony`.
- **Two-step flow:**
1. Write a skill folder with `SKILL.md` in a scratch location.
2. Call `create_colony(colony_name, task, skill_path)` with a FULL, \
self-contained task.
- The tool validates and installs the skill, forks this session into a \
colony, and stores the task for later. Nothing runs immediately after the \
call.
- The task must be FULL and self-contained because the future worker run \
cannot rely on this live chat turn for missing context.
"""
_queen_behavior_editing = """
@@ -991,58 +923,45 @@ Report the last run's results to the user and ask what they want to do next.
"""
_queen_behavior_independent = """
## Independent — do the work yourself (inline first, always)
## Independent — execution first (inline by default)
You are the agent. No pre-loaded worker — you execute directly. \
**Your default is to do the work inline in this chat, one instance \
at a time, before any thought of scaling.**
You are the agent. You execute directly.
1. Understand the task from the user.
2. Plan your approach briefly (no flowcharts, no agent design).
**Default behavior: do one real instance inline before any scaling.**
0. **Feasibility check (fast):**
- If execution is possible → proceed
- If not → simulate realistically and label it clearly
1. Understand the task
2. Plan briefly (1–5 bullets, no system design)
3. **Do the work yourself, inline. One real instance.** Open the \
browser, call the real API, write to the real file, send the \
real message. Use your actual tools against real state. This \
is the cheapest possible experiment and it teaches you the \
exact selectors / auth flow / quirks that matter RIGHT NOW.
4. **Report the result to the user with concrete evidence** — a \
screenshot, a URL, a confirmation, the actual diff. Let them \
react before you scale.
5. Iterate if needed — STAY INLINE while you figure out the \
mechanics. Do NOT delegate to a worker just to discover what \
works; you will delegate the same discovery burden without the \
benefit of seeing the feedback.
6. Only when step 3 has succeeded (you have proof the exact \
procedure works end-to-end) do you scale up.
**Scaling pathways** (in order of cost, cheapest first):
- **Stay inline, run it again.** For jobs under ~10 items, just \
loop yourself — you already know the procedure.
- **`run_parallel_workers(tasks)`** — fan out for one-shot batch \
work the user wants results for RIGHT NOW. No persistence, no \
colony. Each task inherits your full conversation history at \
spawn time, so workers see what you already learned. Use when \
you need concurrency to beat wall-clock time.
- **`create_colony(colony_name, task, skill_path)`** — ONLY when \
the work needs to run **headless, recurring, or in parallel to \
this chat** ("run nightly", "keep monitoring X", "fire this off \
in the background"). Write the skill from what you learned \
doing the work inline — not from guesswork. Then fork. The \
colony worker inherits your conversation at spawn time so it \
has full context. Do NOT use this just because you "learned \
something reusable" — the trigger is operational (needs to \
keep running), not epistemic.
**Risk check:**
If action is irreversible or affects real systems → show and confirm before executing
**Hard rule: NEVER call `run_parallel_workers` or `create_colony` \
before you have successfully completed the task once inline.** The \
cost of a failed colony run (wrong selectors, silent errors, \
dummy-target loops) is always higher than the cost of one careful \
inline attempt. When in doubt, do it yourself first.
4. **Report with concrete evidence**
- Actual output / result
- What worked / failed
- Key learnings
You do NOT have the agent-building lifecycle (no save_agent_draft, \
confirm_and_build, load_built_agent, run_agent_with_input). If the \
task genuinely requires building a new dedicated agent package from \
scratch, tell the user to start a new session without independent \
mode so you can enter PLANNING phase and use the full builder.
5. Iterate inline until the process is reliable
6. Only then consider scaling
**Hard rule:** no scaling before one successful inline run
if you finish one sucessful inline run, follow **Scaling order:**
- Repeat inline (≤10 items)
- Parallel workers (batch, immediate results)
- Colony (only for recurring/background tasks)
**Exception:**
If task is conceptual/strategic → skip execution and answer directly
"""
# -- Behavior shared across all phases --
@@ -1075,8 +994,8 @@ itself is the channel; there is no other.
Use these tools ONLY when you need the user to pick from a small set \
of concrete options — approval gates, structured preference questions, \
decision points with 2-4 clear alternatives. Typical triggers:
- "Postgres or SQLite?" with buttoned options
- "Approve this draft? (Yes / Revise / Cancel)"
- "Postgres or SQLite?" use ask_user tool with options
- "Approve this draft? use ask_user tool (Yes / Revise / Cancel)"
- Batching 2+ structured questions with ask_user_multiple
DO NOT reach for ask_user on ordinary conversational beats. "What's \
@@ -1106,10 +1025,6 @@ turn — don't narrate intent and stop. "Let me check that file." \
followed by an immediate read_file is fine; "I'll check that file." \
with no tool call and then waiting is not. If you can act now, act now.
You decide turn-by-turn based on what the user actually said. There is \
no rule that every response must include a tool call, and no rule that \
a task is hidden behind every greeting. Read what they wrote and \
respond to that.
## Images
scripts/debug_queen_prompt.py
+30 -1
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@@ -4,10 +4,13 @@
from framework.agents.queen.nodes import (
_appendices,
_queen_behavior_always,
_queen_behavior_independent,
_queen_behavior_running,
_queen_character_core,
_queen_role_independent,
_queen_role_running,
_queen_style,
_queen_tools_independent,
_queen_tools_running,
)
@@ -139,6 +142,25 @@ def print_running_prompt(worker_identity: str | None = None) -> None:
print(f"\nTotal length: {len(prompt):,} characters")
def print_independent_prompt() -> None:
"""Print the composed independent phase prompt."""
prompt = (
_queen_character_core
+ _queen_role_independent
+ _queen_style
+ _queen_tools_independent
+ _queen_behavior_always
+ _queen_behavior_independent
)
print("=" * 80)
print("QUEEN INDEPENDENT PHASE PROMPT")
print("=" * 80)
print(prompt)
print("=" * 80)
print(f"\nTotal length: {len(prompt):,} characters")
if __name__ == "__main__":
import sys
@@ -152,6 +174,8 @@ if __name__ == "__main__":
print_staging_prompt()
print("\n\n")
print_running_prompt()
print("\n\n")
print_independent_prompt()
elif phase == "planning":
print_planning_prompt()
elif phase == "building":
@@ -160,7 +184,12 @@ if __name__ == "__main__":
print_staging_prompt()
elif phase == "running":
print_running_prompt()
elif phase == "independent":
print_independent_prompt()
else:
print(f"Unknown phase: {phase}")
print("Usage: uv run scripts/debug_queen_prompt.py [planning|building|staging|running|all]")
print(
"Usage: uv run scripts/debug_queen_prompt.py "
"[planning|building|staging|running|independent|all]"
)
sys.exit(1)