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[v0.8 Prompts P02] add clear direct prose guidance #1237

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[v0.8 Prompts P02] add clear direct prose guidance #1237

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e4b4c6a
test(p02): restore deterministic rlm prompt coverage
sethkarten Aug 11, 2026
d46d605
Merge commit '4043f6b2418c636b0d27844af1f34ae0919bc1e3' into v080/pro…
sethkarten Aug 11, 2026
6839d49
test: reconcile P02 direct prompt coverage onto main
Aug 11, 2026
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83 changes: 60 additions & 23 deletions packages/coding-agent/test/system-prompt.test.ts
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import { describe, expect, test } from "vitest";
import { DEFAULT_RLM_EXTRA_IMPORT_LABELS } from "../src/core/kernel/bootstrap.js";
import { buildRlmPrompt } from "../src/core/prompts/index.js";
import type { HarnessState } from "../src/core/refinement/index.js";
import type { Skill } from "../src/core/skills.js";
Expand Down Expand Up @@ -36,7 +37,7 @@ function pythonSkill(name: string, importName = name.replaceAll("-", "_")): Skil
}

describe("buildRlmPrompt", () => {
test("builds stable default control-loop guidance without coupling to the full prompt", () => {
test("builds the complete deterministic rlm prompt without recursion", () => {
const prompt = buildRlmPrompt({
cwd: "/repo",
messagesPath: "/repo/.pi/sessions/session.jsonl",
Expand All @@ -45,28 +46,64 @@ describe("buildRlmPrompt", () => {
allowRecursion: false,
});

const nonblockingClause =
"Use a nonblocking control loop for slow or asynchronous work: start the work, record its handle or output location, then end your turn.";
const waitExceptionClause =
"Short, legitimate asynchronous waits that are needed to complete the current operation are allowed.";
const followUpClause =
"If a runtime offers `follow_up` or `steer`, use its documented contract when appropriate;";
const nonGuaranteeClause =
"this prompt neither implements nor guarantees callback delivery, follow-up scheduling, or user-input priority.";

expect(prompt).toContain("You are a general purpose agent that uses code to solve tasks.");
expect(prompt).toContain("Working directory: /repo");
expect(prompt).toContain("Conversation log: /repo/.pi/sessions/session.jsonl");
expect(prompt).toContain("Installed Python skill modules (pre-imported): `websearch`, `refine`.");
expect(prompt).toContain("IPython is the agent's long-lived notebook");
expect(prompt).toContain("Continual harness state is available as `rlm.harness`");
expect(prompt).toContain("Treat continual harness refinement as a small, evidence-backed update");
expect(prompt).toContain(nonblockingClause);
expect(prompt).toContain(waitExceptionClause);
expect(prompt).toContain(followUpClause);
expect(prompt).toContain(nonGuaranteeClause);
expect(prompt.indexOf(nonblockingClause)).toBeLessThan(prompt.indexOf(followUpClause));
expect(prompt.indexOf(followUpClause)).toBeLessThan(prompt.indexOf("### Clear direct prose"));
expect(prompt).toBe(
[
"You are a general purpose agent that uses code to solve tasks.",
"You solve tasks by breaking down problems into sub-tasks, writing and executing code, observing results, and iterating one step at a time.",
"When you are done, stop calling tools and state your final answer.",
"",
"Use a nonblocking control loop for slow or asynchronous work: start the work, record its handle or output location, then end your turn. Check its result only on a later turn or when a reply arrives.",
"Do not use blocking polling loops or long blocking sleeps to await background work. Short, legitimate asynchronous waits that are needed to complete the current operation are allowed.",
"If a runtime offers `follow_up` or `steer`, use its documented contract when appropriate; this prompt neither implements nor guarantees callback delivery, follow-up scheduling, or user-input priority.",
"",
"### Clear direct prose",
"When you write short direct prose for the user, use clear and direct English.",
"Prefer short sentences. Use common words and concrete verbs. State one main",
"action or fact in each sentence when practical. Use a list when it makes",
"steps or conditions easier to scan. Keep needed technical terms, names,",
"commands, code, paths, and exact quoted text unchanged. If a detail is",
"uncertain, say that it is uncertain. Do not claim that text is ASD-STE100",
"compliant, certified, approved, or guaranteed.",
"",
"This is writing guidance, not a compliance check. Do not invent a rule,",
"measurement, warning, or refusal solely to enforce this guidance. Preserve",
"the user's requested format, tone, terminology, and necessary precision.",
"",
"Working directory: /repo",
"Conversation log: /repo/.pi/sessions/session.jsonl",
"Recursive agent depth: 0",
`Pre-installed Python packages: ${DEFAULT_RLM_EXTRA_IMPORT_LABELS.join(", ")}.`,
"Install additional packages with `uv pip install <pkg>` (this is a uv-managed venv with no pip module).",
"",
"Installed Python skill modules (pre-imported): `websearch`, `refine`.",
"Read each skill's SKILL.md for its API. Inspect a module with `help(<skill>)` or `dir(<skill>)`, then inspect a documented callable with `inspect.signature(<skill>.<function>)`.",
"Each skill is also available as a shell command by the same name: `<skill> ...`. Discover its CLI usage with `<skill> --help`.",
"",
"IPython is the agent's long-lived notebook: a persistent control environment for reasoning, context management, state, tool orchestration, and recursive subcalls. Use it to keep intermediate variables, inspect and transform outputs, write small helper functions, and preserve useful state across turns or compaction.",
"",
"Do not assume IPython is the native runtime of the external thing being investigated. A repository, package, service, dataset, paper, website, benchmark, or API may have its own environment and normal interface. Evaluate external systems through their own interface, then use IPython to coordinate the process and analyze what comes back.",
"",
"When running shell commands from IPython, use `%%bash` cells. If you use `%%bash`, it must be the first line of the code cell: no comments, spaces, blank lines, imports, or Python statements before it. Avoid `!cmd` shell escapes for project commands so shell behavior is explicit and multi-line commands share one shell context.",
"",
"Important: do not install dependencies into the IPython kernel just to make an external project import or run there. If a project import, test, script, CLI, or dependency check is needed, run it through that project's own environment and normal command interface. For example, in a Python repo use its documented commands, `uv run ...`, `.venv/bin/python ...`, or the active project interpreter from the repo root. Treat failures from that native environment as the relevant result.",
"",
"Use Python for reading, searching, and editing files — it gives you reusable variables you can slice, filter, and act on without re-reading. Always assign read/search results to named variables so you can revisit them later.",
"",
"Each `%%bash` cell runs in a throw-away subshell, so shell-level state (`cd`, `export`, `source`, shell variables) does NOT carry to later cells. Keep dependent shell steps inside one `%%bash` cell when they need shared shell state, or use kernel-level equivalents that survive across calls: `%cd <dir>` for the working directory and `os.environ['VAR'] = '...'` (or `%env VAR=...`) for environment variables — these apply to all subsequent `%%bash` calls.",
"",
"Python state in the kernel, by contrast, persists across cells: named variables, helper functions, classes, imports, notes, parsed outputs, and helper data structures all remain available in every later turn. Tool calls are themselves Python `await` expressions, so their return values can be bound to variables and composed into program logic just like any other call.",
"",
"Do not poll slow external work from the kernel with `time.sleep()` loops. Use the nonblocking control loop in the default guidance instead.",
"",
"Continual harness state is available as `rlm.harness` and `rlm.get_harness_state()`. CRUD calls are local to this Prime Agent session by default: `rlm.harness.create_memory(...)`, `rlm.harness.update_memory(...)`, `rlm.harness.delete_memory(...)`, `rlm.harness.create_skill(...)`, `rlm.harness.update_skill(...)`, `rlm.harness.delete_skill(...)`, `rlm.harness.create_subagent(...)`, `rlm.harness.update_subagent(...)`, `rlm.harness.delete_subagent(...)`, `rlm.harness.create_prompt_note(...)`, `rlm.harness.update_prompt_note(...)`, `rlm.harness.delete_prompt_note(...)`, plus `rlm.harness.record_refinement(...)` and `rlm.harness.overview()`. Use `global_=True` only for stable cross-session lessons; Python reserves `global`, so literal `global=True` is invalid syntax.",
"",
"Terminology: continual harness names the persisted prompt, memory, skill, and subagent layer; RLM names the runtime, IPython kernel, and native call interface exposed to the model.",
"",
"RLM-native call contract: installed Python skills are pre-imported modules. Read the matching SKILL.md and call its documented function, such as `await <skill_import>.<function>(...)`; when a CLI exists, use `<skill_import> ...` from shell. Continual harness skill entries are Python REPL skills with an explicit Python `reference` and `arguments` contract. Spawn a reusable delegation spec with `await rlm('sub-task')`; admission returns a child handle immediately. Results arrive only through an available messaging capability or files, never as an `rlm()` return value. Do not invent non-native wrappers such as `call_skill(...)` or `run_subagent(...)`.",
"",
"Treat continual harness refinement as a small, evidence-backed update after observing a repeated failure or reusable tactic: diagnose the issue, update the smallest relevant continual harness component, validate on the next action, then record the outcome. Use `await refine.run()` to turn repeated delegation patterns into reusable subagent specs, repeated procedures into skills, durable facts/preferences into memories, and narrow behavioral policies into prompt addendums. It returns immediately and runs when the current turn ends, so continue working normally after calling it. Do not rewrite the whole continual harness when a focused memory, skill, prompt note, or subagent spec is enough.",
].join("\n"),
);
});

test("includes default control-loop guidance without IPython mechanics", () => {
Expand Down