abc/analysis_claude_code
1
0
Fork 0
mirror of https://github.com/shareAI-lab/analysis_claude_code.git synced 2026-09-21 21:03:38 +08:00
Code Issues Packages Projects Releases Wiki Activity

docs: make workflow and goal lessons harness-first

Browse Source
This commit is contained in:
Haoran
2026-07-30 20:05:33 +08:00
parent cb8fae1bdd
commit 4bc33ec858
12 changed files with 115 additions and 207 deletions
Download Patch File Download Diff File Expand all files Collapse all files

21
s22_goal_loop/code.py
View File

@@ -1,9 +1,5 @@
"""
s22_goal_loop — /goal session goal loop (teaching version)
Clean-room behavioral reconstruction of Claude Code's `/goal` command. Grounded
in @anthropic-ai/claude-code@2.1.177 observed behavior
(reverse-research/cc_goal_loop), NOT leaked source.
s22_goal_loop — minimal /goal session loop for a teaching harness
Idea:
s01-s21 end a turn when the model emits no tool_use. `/goal` adds a
@@ -27,12 +23,12 @@ Idea:
Run:
python code.py # /goal until tests pass + deploy green; watch the gate
Teaching simplifications (vs real /goal and runtime.mjs):
Implementation choices:
- The evaluator is a deterministic keyword check, not a small/fast model.
- One mock task-notification produces the trusted evidence; the loop / monitor
/ background-task plane (s13/s14) is out of scope — this chapter is just the
goal gate.
- The evidence trust boundary is the faithful part: only task-notification /
- The evidence trust boundary is the important part: only task-notification /
monitor-line origins count as evidence, so the `/goal` command text, the
continuation reminder, and plain assistant prose can NOT satisfy the goal.
Ordinary `submit()` calls cannot set those labels; only the host-event
@@ -68,7 +64,7 @@ class Message:
# ============================================================
# CommandQueue — continuation prompts live here (mirrors CommandQueue)
# CommandQueue — continuation prompts live here
# ============================================================
class CommandQueue:
PRIORITY = {"now": 0, "next": 1, "later": 2}
@@ -102,7 +98,7 @@ class CommandQueue:
# ============================================================
# GoalRuntime — the turn-completion gate (mirrors GoalRuntime)
# GoalRuntime — the turn-completion gate
# ============================================================
class GoalRuntime:
def __init__(self, transcript, queue):
@@ -148,9 +144,8 @@ class GoalRuntime:
return "\n".join(out)
def goal_satisfied(self):
# Real Claude Code routes this to a small/fast evaluator model reading
# the evidence window. The teaching version is a deterministic keyword
# check so the lifecycle is reproducible.
# A production harness can route this evidence window to a separate
# evaluator model. The demo uses a deterministic keyword policy.
objective = self.active["objective"].lower()
evidence = self.evidence_text().lower()
wants_tests = "test" in objective
@@ -193,7 +188,7 @@ class GoalRuntime:
# ============================================================
# Session — the main loop host with a Stop gate (mirrors submit / drain)
# Session — the main loop host with a Stop gate
# ============================================================
class Session:
def __init__(self):