Versioned Skills: Unit A Exercises

Student edition · 90 minutes of dedicated work · 2026-09-09

This is one of two practical units for Chapter 6. Unit A constructs and connects the mechanism; Unit B investigates a controlled failure, repairs it and transfers the invariant. Each is a complete ninety-minute session, with its own setup and required conceptual introductions. Basic Python variables, conditions, loops, functions, lists and dictionaries are the starting knowledge. Libraries and specialized concepts used here are introduced below before the main task.

By the end you should be able to:

1. Explain the chapter's mechanism using a prediction and an observed intermediate result.
2. Construct context assembly from active eligible skills, explicit preferences and revision-matching completed history. Enforce the byte budget and retain provenance before adding the current user request.
3. Solve admit only complete skill requirements using changed inputs and an independent expectation.
4. Retain your implementation, failed/corrected observations, causal explanation and limits.

Installation is preparation time. These are planning estimates, not measured completion times. Use the reference primers when a term is unfamiliar; in Unit B retrieve an explanation before re-reading it. Run All checks that the artifact executes. Unfinished student functions deliberately produce NEEDS_WORK. Keep your first attempt before opening answers.

This notebook belongs to the nineteen-chapter edition. Its supplied teaching runtime is embedded, so it can run without the textbook or another notebook. Where code uses REFERENCE_LESSON, that is the frozen runtime exercise identifier; the reader-facing chapter and saved unit identifiers use the current edition. Building against a supplied runtime is not proof that you have constructed all of its dependencies.

Run the self-contained setup

Use a Python 3.14 Jupyter kernel and Pydantic 2. If needed, run %pip install "pydantic==2.13.4" once in a separate cell and restart the kernel. Package installation needs internet; the lesson itself needs no repository download, API key or prior notebook. The complete source runtime uses Python 3.14, so this edition does not claim compatibility with a hosted notebook service's default interpreter.

The collapsed cell contains 89 frozen teaching files. Base85 represents compressed bytes as text; zlib decompresses them; SHA-256 checks that the decoded files match this edition. These are supplied packaging operations, not learner algorithms. tempfile creates an isolated working copy; Path handles file locations; sys.path tells Python where the supplied modules live. The code is available for inspection below and performs no package installation itself. The subsequent lesson teaches the libraries used by the mechanisms you will implement.

Run setup on every fresh kernel. It writes scratch runtime files separately from your retained practical-work/ch06-a folder. Rerunning setup restores the frozen support files and keeps your saved work. Restarting a kernel clears variables, not saved submission files. Source basis: Sovereign Agent 444c5f6. Some tasks use reviewed local subprocesses; they are not an OS sandbox.

Supplied offline setup and teaching files

The complete supplied setup cell is in the downloadable notebook. Run it there before attempting the cells below. This web edition omits only that compressed setup payload.

Commit to a prediction before the examples

prediction_notes = {
    "prediction": "Write the expected behavior before running the worked example.",
    "reason": "Name the input and rule behind that prediction.",
    "falsifier": "Name an observation that would prove the explanation wrong.",
    "revision": "After execution, explain what changed in your understanding.",
}

Reading the Python vocabulary used in this notebook

You need basic assignments, if, loops, functions, lists and dictionaries. The less familiar features used by the supplied code are introduced here. A library is reusable code that Python can import. The standard library ships with Python; Pydantic is an additional package. An import makes a name available, but does not mean that you have completed the exercise.

JSON is text for exchanging structured values. A Python dictionary is an in-memory object; the JSON representation is a string. Use json.dumps to encode and json.loads to decode. Decoding proves that text has valid JSON syntax, not that its fields match our business contract. Predict which of the following two decoded objects could describe a stock count.

import json

intro_data = {"sku": "MANGO", "count": 3}
intro_text = json.dumps(intro_data, sort_keys=True)
print(type(intro_data).__name__, type(intro_text).__name__, intro_text)
print(json.loads(intro_text))
print("Also valid JSON:", json.loads('["not", "a", "stock", "record"]'))
assert json.loads(intro_text) == intro_data

The first result is a dictionary; the second is a list. Before indexing a decoded object, check the shape that your function promises to accept. An exception interrupts the normal path. raise ValueError(...) refuses an invalid value; try/except lets a caller inspect that expected refusal. Catch the expected class, rather than turning every programming error into apparent success. finally runs cleanup even when an earlier operation raises.

An annotation, such as count: int, documents the expected type. It does not by itself enforce the type at runtime. A class defines a kind of object; an instance holds one object's data. @dataclass asks Python to generate routine construction and comparison methods from annotated fields. frozen=True prevents ordinary reassignment of the instance's fields; it does not make every object nested inside those fields immutable. A method is a function attached to a class; self refers to the instance receiving the call.

from dataclasses import dataclass


@dataclass(frozen=True)
class IntroObservation:
    operation: str
    count: int


intro_observation = IntroObservation("count-mango", 3)
print(intro_observation.operation, intro_observation.count)
assert intro_observation == IntroObservation("count-mango", 3)

A callback is a function passed to another function. This is how the classroom harness invokes your implementation. The argument candidate below is a function object; parentheses perform the call. Predict the two answers before execution, then trace the result to the callback.

def intro_apply(candidate, value):
    return {"input": value, "observed": candidate(value)}


def intro_double(value):
    return value * 2


print(intro_apply(intro_double, 3))
print(intro_apply(lambda value: value + 2, 3))
assert intro_apply(intro_double, 3)["observed"] == 6

lambda value: value + 2 is a small anonymous function. A closure is a function that retains access to values from its surrounding scope. It can bind a tool to a shop snapshot. A shallow copy duplicates only the outer container; copy.deepcopy also copies nested containers used in these fixtures. A set stores distinct values; required <= allowed asks whether every required item is allowed. frozenset is the corresponding immutable set. A tuple groups ordered values; (value,) is a one-item tuple, including the comma.

Paths and cleanup. Path represents a filesystem location. path / "file.json" constructs a child path; read_text and write_text read and write text. A context manager, used with with, manages entry and exit. A temporary-directory context removes its contents on exit. Save your submission outside temporary runtime directories. Reopening a file is different from reusing a Python variable: the former tests retained bytes, while the latter only tests this kernel.

from pathlib import Path
from tempfile import TemporaryDirectory

with TemporaryDirectory() as intro_folder:
    intro_path = Path(intro_folder) / "observation.json"
    intro_path.write_text(json.dumps(intro_data), encoding="utf-8")
    intro_reopened = json.loads(intro_path.read_text(encoding="utf-8"))
    assert intro_reopened == intro_data
    print("Read from a file:", intro_reopened)

Retrieval check: explain JSON versus a dictionary, annotation versus validation, class versus instance, and defining a callback versus invoking it. Change the callback above so an incorrect implementation visibly changes the observed output. This distinction will matter when grading your connected work. Reference: Python's JSON, dataclasses, and pathlib documentation.

Pydantic: turn an input dictionary into a checked object

Pydantic is an additional Python library for validating data. Its model is a class describing fields, not a neural network. Inherit from BaseModel, declare annotated fields, then call model_validate on incoming data. A field without a default is required. A field with a default can be omitted. The result is an instance whose values you read with dot notation.

from pydantic import BaseModel, ConfigDict, Field, ValidationError


class IntroCourseRequest(BaseModel):
    model_config = ConfigDict(strict=True, extra="forbid")
    name: str = Field(min_length=1)
    quantity: int = Field(gt=0, le=1000)
    note: str = ""


intro_request = IntroCourseRequest.model_validate({"name": "mango", "quantity": 4})
print(intro_request.name, intro_request.quantity, repr(intro_request.note))
assert intro_request.note == ""

The annotation says the field's type. Field supplies constraints: gt=0 means greater than zero, le=1000 means at most 1000, and min_length=1 excludes an empty name. ConfigDict sets model-wide behavior. strict=True rejects conversions for this integer field, including "4", 4.0 and True; extra="forbid" rejects undeclared keys. Pydantic can otherwise convert some compatible inputs, so choose this boundary deliberately rather than assuming every accepted input arrived in the expected type.

Predict which rule refuses each payload. ValidationError reports a failed contract. Its errors() entries contain loc, the field location, and type, the failure category. Catching that expected exception lets the notebook inspect the failure and continue.

intro_bad_requests = [
    {"name": "mango", "quantity": "4"},
    {"name": "mango", "quantity": True},
    {"name": "", "quantity": 4},
    {"name": "mango", "quantity": 0},
    {"name": "mango", "quantity": 4, "approved": True},
    {"quantity": 4},
]
for intro_bad_request in intro_bad_requests:
    try:
        IntroCourseRequest.model_validate(intro_bad_request)
    except ValidationError as intro_error:
        print([(item["loc"], item["type"]) for item in intro_error.errors(include_input=False)])
    else:
        raise AssertionError("An invalid input crossed the declared contract")

Use model_dump() for a Python dictionary, model_dump_json() for JSON text, and model_validate_json() to parse and validate JSON. model_json_schema() describes the contract; it is neither an instance's current values nor an invocation of the business handler.

intro_serialized = intro_request.model_dump_json()
intro_schema = IntroCourseRequest.model_json_schema()
assert IntroCourseRequest.model_validate_json(intro_serialized) == intro_request
print("Actual values:", intro_request.model_dump())
print("Quantity contract:", intro_schema["properties"]["quantity"])
assert intro_schema["properties"]["quantity"]["exclusiveMinimum"] == 0

Four is valid input to this schema even if the shop needs six. Pydantic checks the declared shape and constraints; the handler still needs authoritative stock, price and permission. Ordinary assignments to an existing instance are not automatically revalidated unless configured for assignment validation. This lesson validates new input at the boundary and uses the resulting values. Explain these limits before relying on a model object in a transaction or tool call.

Chapter 2's full introduction expands this pattern with a separate data-repair checkpoint. This notebook contains the required pattern here so prior Pydantic experience is not needed. References: models, fields, and strict mode.

SQLite from the first row to an atomic change

A dictionary disappears when its process ends. A database can retain records so a later process can resume from evidence. SQLite is an embedded database: Python's sqlite3 library opens a local database file without starting a separate database server. SQL is the language used to define, select and change its records. A table has named columns and rows. A primary key identifies a row; a query asks for rows satisfying a condition.

Start with a deliberately small preference table. Read the SQL as instructions: create the table, insert one named value, then select the value for one session. ? is a parameter placeholder; the values are passed separately so they are data, not SQL instructions. fetchone() returns one row or None; it does not guarantee that a matching row exists.

import sqlite3
from pathlib import Path
from tempfile import TemporaryDirectory

with TemporaryDirectory() as intro_sql_folder:
    intro_db_path = Path(intro_sql_folder) / "example.sqlite"
    intro_db = sqlite3.connect(intro_db_path, autocommit=True)
    intro_db.execute("CREATE TABLE preference (id INTEGER PRIMARY KEY, session TEXT, value TEXT)")
    intro_db.execute("INSERT INTO preference (session,value) VALUES (?,?)", ("lucy", "09:00"))
    intro_row = intro_db.execute(
        "SELECT value FROM preference WHERE session=?", ("lucy",)
    ).fetchone()
    print("Matching row:", intro_row)
    assert intro_row == ("09:00",)
    assert (
        intro_db.execute(
            "SELECT value FROM preference WHERE session=?", ("another-session",)
        ).fetchone()
        is None
    )
    intro_db.close()
    intro_reopen = sqlite3.connect(intro_db_path, autocommit=True)
    assert intro_reopen.execute("SELECT count(*) FROM preference").fetchone()[0] == 1
    intro_reopen.close()
print("The row survived closing and reopening its connection.")

Index [0] selects the first column of a returned tuple. sqlite3.Row is an alternative row factory that also permits named-column access. dict(row) then produces an ordinary dictionary. The book's Database wrapper supplies that configuration and its schema; the wrapper is course code, while sqlite3 is the standard library. You will use the public connection and transaction methods explained at the exercise boundary, rather than needing to reconstruct the wrapper.

Now consider a budget. Moving five pence from reserved to spent requires two values to change together. A transaction makes a group of local changes commit together or roll back together. The example explicitly controls SQL transactions with autocommit=True and SQL statements. BEGIN IMMEDIATE starts a write transaction; COMMIT keeps its changes; ROLLBACK discards them. Predict the row after the deliberately raised exception. Catching an error alone would not undo the first update; the rollback is the operation that restores the prior state.

intro_ledger = sqlite3.connect(":memory:", autocommit=True)
intro_ledger.execute(
    "CREATE TABLE budget (id INTEGER PRIMARY KEY, reserved INTEGER, spent INTEGER)"
)
intro_ledger.execute("INSERT INTO budget VALUES (1,5,0)")
intro_ledger.execute("BEGIN IMMEDIATE")
try:
    intro_ledger.execute("UPDATE budget SET reserved=0 WHERE id=1")
    raise ValueError("injected failure before the matching spend update")
except ValueError:
    intro_ledger.execute("ROLLBACK")
assert intro_ledger.execute("SELECT reserved,spent FROM budget").fetchone() == (5, 0)
intro_ledger.execute("BEGIN IMMEDIATE")
intro_ledger.execute("UPDATE budget SET reserved=0,spent=5 WHERE id=1")
intro_ledger.execute("COMMIT")
print(
    "After a complete change:", intro_ledger.execute("SELECT reserved,spent FROM budget").fetchone()
)
intro_ledger.close()

The literal :memory: creates a temporary database inside this connection; it is useful for the small experiment, but the earlier file example establishes persistence. Neither example proves that a remote supplier rolls back when the local transaction rolls back. An external operation has its own state and evidence.

SQL you will meet later. UPDATE ... SET ... WHERE ... changes selected rows. AND combines conditions. ORDER BY makes an ordering explicit; absent that clause, do not rely on row order. count(*) counts rows; sum(amount) totals a column and can be NULL on an empty input; coalesce(sum(amount),0) uses zero for that empty aggregate. A UNIQUE constraint rejects duplicate identities. GROUP BY status computes one aggregate per status.

An invariant is a condition that must remain true across operations, such as nonnegative reserved money. A snapshot is a consistent view at one point; two separate reads can describe different moments unless their transaction contract binds them. In the book, with db.immediate() groups related writes. It is a course-defined context manager with the commit/rollback purpose you just observed. Do not assume that an arbitrary with connection has identical behavior under every SQLite autocommit setting.

Your prediction: two workers both read ten remaining pence outside a transaction and each approve seven. Why can both believe the next order fits? Explain what must be checked together with the write. Then change the example's initial reserved amount and repeat the failure. Reference: Python's SQLite tutorial and transaction control.

A skill is versioned guidance with declared requirements

Lucy wants the same opening check every morning: inspect stock, review deliveries and prepare drafts for shortages. A skill packages a reusable procedure with a name, version and declared tool requirements. Its text guides a model; it does not create a new Python capability or grant permission to call a registered tool. Keep “what the procedure requests” separate from “what this worker is allowed to execute.”

The book stores readable skill definitions in TOML, a configuration format with named keys, strings, arrays and tables. Python's tomllib reads TOML into ordinary Python objects. The loads method reads a string; load reads a binary file. Parsing checks syntax, while your application still checks required fields and meaning. You do not need a separate TOML package on Python 3.14.

Read one procedure as data

Predict the Python type of requires, then compare the required capabilities with the worker's allowlist. A subset test means all requirements must be available; a nonempty intersection would prove only that at least one requirement is available.

import tomllib

intro_skill = tomllib.loads("""
name = "opening-review"
version = 2
requires = ["list_stock", "supplier"]
instructions = "Inspect stock and compare the supplier quote before drafting."
""")
intro_required = set(intro_skill["requires"])
for intro_allowed in ({"list_stock"}, {"list_stock", "supplier"}, set()):
    print(sorted(intro_allowed), "eligible:", intro_required <= intro_allowed)
assert not intro_required <= {"list_stock"}
assert intro_required <= {"list_stock", "supplier"}

A staged version is available for inspection and evaluation. An active version is the one admitted into current context. Staging alone should not change behavior. Keeping versions immutable lets an evaluation name the exact procedure it tested. If a file's contents change under the same version identity, the evidence no longer has a stable subject.

Build context within an explicit byte budget

Context is the input assembled for the next model request: selected skills, current preferences, relevant history and the new user request. More context is not automatically better. A limit forces an explicit admission policy and prevents an unbounded history from consuming the budget. This implementation measures UTF-8 encoded JSON bytes, which differ from characters and model tokens. Predict why the non-ASCII example below has a larger encoded length.

import json

for intro_text in ("cafe", "café", "🍦"):
    intro_bytes = intro_text.encode("utf-8")
    print(repr(intro_text), "characters:", len(intro_text), "UTF-8 bytes:", len(intro_bytes))
intro_items = [{"kind": "skill", "text": "Inspect stock."}, {"kind": "memory", "text": "10:00"}]
intro_context = []
intro_limit = 60
for intro_item in intro_items:
    intro_candidate = [*intro_context, intro_item]
    if len(json.dumps(intro_candidate, ensure_ascii=False).encode("utf-8")) <= intro_limit:
        intro_context = intro_candidate
print("Admitted context:", intro_context)
assert len(json.dumps(intro_context, ensure_ascii=False).encode("utf-8")) <= intro_limit

Notice that the example measures the complete candidate representation, including JSON brackets, keys and separators. Adding the lengths of text values alone would undercount it. The actual exercise preserves provenance and the declared ordering while admitting only bounded items. It does not silently truncate an instruction halfway through a sentence and call that the same procedure.

History has a configuration boundary

A completed old conversation may have used another preference or skill revision. Replaying it as current guidance can reintroduce an obsolete decision. The context builder therefore needs to select revision-matching completed history. “Completed” concerns that prior episode's state; it is not proof that every sentence it produced was factually correct.

In the core exercise, the context function assembles active eligible skills, explicit preferences and bounded history. Its caller is the model-request path. Unit B removes the requirement check and demonstrates that an active skill requiring an unavailable tool enters context. The repair belongs in eligibility. Strengthening the skill's prose cannot replace the missing set condition.

Explain before constructing: what differs between a malformed TOML file, a staged but inactive skill, an active ineligible skill and an active eligible skill that does not fit the byte budget? Give one observation for each. Then write a new procedure with no tool requirements and explain why an empty required set is eligible even for an empty allowlist. Eligibility still grants no new capabilities. Reference: Python's tomllib documentation.

Understand the supplied execution interface

The course runtime is provided so your implementation can be connected to real callers and storage. SourceTask(ROOT, chapter) makes a private copy. install(source) replaces only the declared function; visible() invokes the real chapter probe; save(path, result) retains a successful implementation and its evidence. load(path) checks the saved identities and hashes. inject_failure() changes the declared boundary; repair(fragment) replaces that broken fragment. close() removes the scratch copy after you retain evidence. These methods are supplied harness operations, not additional packages you must discover or install.

RuntimeLab provides the same copied-source failure experiment without the complete-function construction layer. Its run method records exit status, observations and the compared expectation. A subprocess log from an unfinished learner implementation is feedback about that implementation; it is not a successful connection. A syntax error in the notebook cell itself is a separate issue to fix. The task below names which interface it uses.

For direct-function units, the visible driver calls your callback without installing a source string. In either case, trace where your code is invoked. Supplied fixtures, database wrappers and replay models are labelled infrastructure; your own implementation and changed-case explanation are the evidence of learning.

Main practical: construct, connect and challenge

Design question: Guidance cannot create capability?

Construct context assembly from active eligible skills, explicit preferences and revision-matching completed history. Enforce the byte budget and retain provenance before adding the current user request.

You edit a complete function in a temporary copy of src/sovereign_agent/assistant_context.py. The real callers, database and tool boundaries remain connected. The self-contained setup above supplies the frozen development runtime. The notebook runs reviewed local subprocesses; it is not a security sandbox. No live account is required.

Predict before running

Stage and activate a local skill through the existing API, then inspect context with and without its required tool capability.

Write the expected result and a falsifying observation before running. Include one legal action, one refusal, and the exact-empty case where the interface permits it. Explain the consequence for Lucy if your prediction is wrong.

import json
import os
import runpy
from pathlib import Path

ROOT = COURSE_ROOT

SourceTask = runpy.run_path(str(ROOT / "book/always_on/exercises/source_tasks_v1.py"))["SourceTask"]
REFERENCE_LESSON = 5
HANDOFF = Path("ch06-unit-a-handoff-v1.json")

Construct the complete mechanism

Implement context in the string below. Keep the named signature and existing helper interfaces. The starter is intentionally incomplete; its failed connection is reported separately from whether the notebook itself executed. A constant answer cannot stand in for the real mechanism.

Inspect the supplied caller around the function in src/sovereign_agent/assistant_context.py. Draw the data path from the observed output through this function to its actual input or query. Then write your implementation from the contract.

implementation_source = """
def context(
    db: Database, session: str, prompt: str, *, allowed: frozenset[str], byte_budget: int = 16_384
) -> list[dict[str, Any]]:
    raise NotImplementedError("Construct this chapter mechanism")
"""

Hint 1 — the design

An active procedure may require a tool the current worker cannot call.

Hint 2 — the boundary

Inspect the parameters and the caller in src/sovereign_agent/assistant_context.py. Identify validation, durable state and the first externally observable effect. Preserve the existing surrounding helper contracts.

Hint 3 — the structure

Read active rows; require the full requires set; append preferences and bounded matching history; admit items only within the JSON byte budget.

Connect to the cumulative runtime

The following installs your complete function into the copied runtime and executes the chapter probe against it. It saves your implementation and the observed connection for Unit B only after that connection succeeds.

def connect_build(source):
    task = SourceTask(ROOT, REFERENCE_LESSON)
    try:
        task.install(source)
        result = task.visible()
        if result["status"] == "PASS":
            task.save(HANDOFF, result)
        return result
    finally:
        task.close()


build_result = connect_build(implementation_source)
print("CONNECTION", build_result["status"])
print("OBSERVATION", build_result["observation"])

Challenge and transfer

Compare staged-only, active-empty-requirements, partially permitted and fully permitted skills. A prompt must never expand the dispatcher allowlist.

Use a fresh SourceTask, install your implementation and edit only its copied probe to run the changed input. Keep the expected result in your prediction notes, independent of your implementation. Call task.run("MY_TRANSFER", expected=your_expected) and close the task in finally. Retain both a valid and a refused case so rejecting everything cannot pass.

The instructor runs additional cases with different identities and boundaries against the real source. Passing the visible connection alone is not the transfer verdict. Do not put instructor solutions or holdouts into a student submission.

Save and explain

After success, submit ch05-unit-a-handoff-v1.json, your source, prediction notes and changed-input observations. Unit B checks the chapter, runtime hash and exact implementation hash and re-executes your code.

Explain which input or state caused the output, which observation would refute the explanation and what remains outside the guarantee: Eligibility and fixture activation checks do not prove live model quality.

exercise_report = {
    "unit": "ch06-a",
    "attempted": 1,
    "completed": int(build_result["status"] == "PASS"),
    "failed": int(build_result["status"] != "PASS"),
    "skipped": 0,
    "connection": build_result["status"],
    "handoff": "WRITTEN" if build_result["status"] == "PASS" else "NOT_READY",
}
print("EXERCISE_REPORT=" + json.dumps(exercise_report, sort_keys=True))

Changed-constraint construction: Admit only complete skill requirements

Allow twenty minutes. Spend three minutes predicting, ten implementing and tracing, five on a new case of your own, and two explaining the surviving limitation. This is dedicated work, not an invitation to run a supplied answer. Both units revisit the same invariant after different core experiences; in Unit B, attempt this task from memory before consulting Unit A.

Implement transfer_check(skills, allowed). Return names, preserving input order, for skills with active=True whose complete requires list is a subset of allowed. Inputs have unique names. An empty requirement list is eligible even when allowed is empty. Never change allowed based on instruction text.

Write your expected values before running the table. Keep one accepted case and one refusal. Your function is passed directly into the driver below. The driver copies inputs and checks they remain unchanged; it does not replace your implementation with the reference answer.

Hint 1 — identify the authoritative inputs Name the source field for each output value. Which input changes while the rule remains the same?

Hint 2 — choose the boundary cases Start with exact empty, exact equality and one value on each side of the boundary where valid. Do not add a special case for a visible product name or operation identity.

def transfer_check(skills, allowed):
    raise NotImplementedError("Separate active state from capability eligibility")

import copy
import json

TRANSFER_CASES = [
    (
        "partial capabilities",
        [[{"name": "opening", "active": True, "requires": ["stock", "price"]}], ["stock"]],
        [],
    ),
    (
        "all capabilities",
        [[{"name": "opening", "active": True, "requires": ["stock", "price"]}], ["stock", "price"]],
        ["opening"],
    ),
    (
        "empty requirements",
        [[{"name": "greeting", "active": True, "requires": []}], []],
        ["greeting"],
    ),
    ("staged only", [[{"name": "greeting", "active": False, "requires": []}], []], []),
]


def same_transfer_value(actual, expected):
    if type(actual) is not type(expected):
        return False
    if isinstance(expected, dict):
        return actual.keys() == expected.keys() and all(
            same_transfer_value(actual[key], value) for key, value in expected.items()
        )
    if isinstance(expected, list):
        return len(actual) == len(expected) and all(
            same_transfer_value(a, e) for a, e in zip(actual, expected, strict=True)
        )
    return actual == expected


def run_transfer(candidate, cases):
    observations = []
    for label, arguments, expected in cases:
        supplied = copy.deepcopy(arguments)
        before = copy.deepcopy(supplied)
        raised = None
        try:
            actual = candidate(*supplied)
        except NotImplementedError:
            raised = "NotImplementedError"
            actual = {"unfinished": True}
        except Exception as error:
            raised = type(error).__name__
            actual = {"raises": raised}
        expects_error = isinstance(expected, dict) and set(expected) == {"raises"}
        correct = (
            raised == expected["raises"]
            if expects_error
            else (raised is None and same_transfer_value(actual, expected))
        )
        passed = correct and same_transfer_value(supplied, before)
        observations.append(
            {"case": label, "expected": expected, "observed": actual, "passed": passed}
        )
        print("PASS" if passed else "NEEDS_WORK", label, "expected", expected, "observed", actual)
    return observations


transfer_observations = run_transfer(transfer_check, TRANSFER_CASES)
TRANSFER_PASSED = all(row["passed"] for row in transfer_observations)
print("TRANSFER_STATUS", "PASS" if TRANSFER_PASSED else "NEEDS_WORK")

Design a counterexample and retrieve the mechanism

Add one new case with an independently calculated expected outcome to TRANSFER_CASES and rerun the driver. Change one condition at a time. Then deliberately replace your candidate with a constant answer in a temporary copy and show a case that rejects it. Restore your implementation. Explain why that counterexample is stronger than repeating the original example with a new name.

Without viewing the worked example, write the invariant in words and trace one observed value back to its input. Identify which part is a local fixture result and which claim would need a live provider, host or external-system observation. Keep a first attempt even if you used a hint.

Save your evidence and explain the result

Fill the prediction notes and your explanation before saving. Include the exact observed value, the input or retained row that caused it, your code's invocation point, one failed hypothesis, and the strongest claim the evidence still cannot support. A completed code cell alone does not earn explanation credit. Do not label reference-start behavior as your own Unit A construction.

Keep this edited notebook, the Markdown if used for notes, saved handoff files, and the JSON record below. Your work folder survives scratch cleanup and can be reopened in a new kernel. An instructor can ask for an unseen case after the visible checks; keep your implementation general.

explanation_notes = {
    "causal_trace": "Explain the input, learner invocation and observed result.",
    "failed_hypothesis": "Describe a prediction the evidence changed.",
    "remaining_limit": "Name the guarantee not established by this experiment.",
}
course_submission = {
    "unit": "ch06-a",
    "planned_minutes": 90,
    "starting_evidence": globals().get("HANDOFF_ORIGIN", "INDEPENDENT_UNIT_A"),
    "prediction": prediction_notes,
    "explanation": explanation_notes,
    "core_report": exercise_report,
    "transfer": transfer_observations,
    "explanation_review": "HUMAN_REVIEW_REQUIRED",
}
submission_path = COURSE_WORK / "ch06-a-submission-v1.json"
submission_path.write_text(
    json.dumps(course_submission, indent=2, sort_keys=True), encoding="utf-8"
)
print("Saved evidence:", submission_path)
print(
    "COURSE_REPORT="
    + json.dumps(
        {
            "unit": "ch06-a",
            "transfer_passed": TRANSFER_PASSED,
            "starting_evidence": course_submission["starting_evidence"],
            "edition": "student",
        },
        sort_keys=True,
    )
)

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