Decision protocol for wiring a verify-then-fix loop around a code-editing LLM
agent. The agent edits → runs lint/test → reads the output → fixes → re-runs,
bounded by an iteration cap and an escalation rule. Activates whenever a coder
agent has a verifiable success criterion (exit code, type-checker output,
failing assertion) and the user wants the agent to converge to "green" on its
own. Framework-agnostic — wraps Aider's `--auto-lint`/`--auto-test`, an
OpenHands SWE-Bench loop, a manual LangGraph cycle, or Claude Code's bash
tool just the same.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
Decision protocol for wiring a verify-then-fix loop around a code-editing LLM
agent. The agent edits → runs lint/test → reads the output → fixes → re-runs,
bounded by an iteration cap and an escalation rule. Activates whenever a coder
agent has a verifiable success criterion (exit code, type-checker output,
failing assertion) and the user wants the agent to converge to "green" on its
own. Framework-agnostic — wraps Aider's `--auto-lint`/`--auto-test`, an
OpenHands SWE-Bench loop, a manual LangGraph cycle, or Claude Code's bash
tool just the same.
domain
coder-agent / tool-result-feedback
audience
engineers wiring LLM agents that must converge on a verifiable spec
trigger_keywords
["auto-lint","auto-test","test-fix loop","fix until tests pass","verify-then-fix","iterate until green","agent feedback loop","iteration cap"]
when_to_use
["any code-edit flow with a verifiable success command (pytest, ruff, mypy, eslint, tsc, go test, cargo check)","wrapping a coding agent so it doesn't return until lint+tests are clean","SWE-Bench-style runs (one issue → patch → tests → fix → submit)","CI guardrail where a PR must be green before the agent declares done"]
when_not_to_use
["the success criterion is subjective ('looks good') — there's no signal to feed back","the verifier takes >5 min and you need the agent interactive — async the loop","human review is the gate (use HITL skill instead)","edits are exploratory / WIP — the loop will fight the user's incomplete code"]
Test-Fix Loop · SOP
One-liner: The test result IS the next prompt. Wiring the verifier is
20% of the work; framing its output as a useful feedback message is 80%.
1. 何时激活 (Activation Rules)
Activate this skill when any of the following triggers fire:
The user says "have the agent fix until tests pass", "run lint and tests
automatically", "iterate until green", or invokes aider --auto-test,
cline --yes, or an OpenHands-style headless agent.
The task has a verifiable success command: a non-zero exit code on
failure (pytest, ruff, mypy, eslint, tsc, go test, cargo check, npm run
build, make check, …).
You're wrapping a code-editing LLM in a script/CI step and need to decide:
when does the agent return?
The agent just made an edit and the next message in the loop would be
"here's what the verifier said".
Do not activate when:
Success is subjective (writing prose, designing UX). The loop has no
feedback signal worth replaying.
The verifier is slow + interactive (full E2E suite, multi-min builds).
Either async-ify the loop, or run a fast subset (pytest -x -k changed) in
the loop and gate the slow suite at PR review.
The gate is human approval, not a machine check — use the HITL skill.
2. 核心心智模型 (Core Mental Model)
2.1 The test result IS the next prompt
The agent's next turn is conditioned almost entirely on the message you
inject between edit-N and edit-N+1. That message — formatted from
stdout, stderr, exit_code — is the prompt. The framework labels it
"tool result" or "verifier output" but mechanically it is a user-role message
the LM consumes verbatim.
⇒ Framing the feedback dominates the model choice. A 4000-line raw pytest
dump prompts a worse fix than a 30-line "first failing test, traceback, the
diff you just applied" digest, regardless of the model behind it.
No verifier → no signal; the agent guesses "done".
No capture → the model can't read stderr; tracebacks live in stderr.
No formatting → 25k-token output distracts the model
(see Aider's 25k context-drift threshold).
No iteration bound → infinite loop; the OpenHands SWE-Bench infinite-loop
bug [oh/6357] is the canonical failure case.
2.3 Why a separate skill (vs "just give the agent a bash tool")
Naively: "let the agent run pytest and read the output". This breaks because:
The agent doesn't know which command to run (project-specific).
The agent dumps the full output into context every iteration, blowing
the 25k threshold by iter 3.
The agent has no termination contract — it'll keep trying after the
test passes "to be safe", or keep trying after 30 failures "to be helpful".
The agent makes edits with no audit trail — if iter 4 was the right
fix, you can't bisect because nothing is committed.
The loop is a contract: *verifier wiring + output capture + feedback framing
iteration cap + per-fix git commit*. Treat it as one operation, not five.
2.4 What "green" means
Verifier returns
Interpretation
Next action
exit 0, no diagnostics
True success
Commit + exit loop
exit 0, warnings
Soft success
Commit + log; optionally surface to user
exit != 0, parseable error
Actionable failure
Format → feed back → next iter
exit != 0, unparseable (e.g. segfault, OOM)
Environment / infra failure
Escalate; do not re-prompt the LM
Timeout / hang
Likely infinite loop in code
Kill, format as timeout error, escalate after 1 retry
3. SOP 工作流 (Agentic Protocol)
Step 1 · Wire the verifier command
Pick the cheapest verifier that catches the class of bug you care about.
Cascade from fastest to slowest:
Stage
Command (concrete)
Catches
Typical latency
1. Format
ruff format --check . / prettier --check .
Style
<1 s
2. Lint
ruff check . / eslint .
Style + obvious bugs
1–5 s
3. Type
mypy --strict src/ / tsc --noEmit
Type errors
5–30 s
4. Test
pytest -x --ff / vitest run --bail 1
Behavioural
10 s–min
5. Build
cargo build / go build ./... / npm run build
Link / compile
10 s–min
Rule: bind --lint-cmd and --test-cmd to stages 1–4 combined into one
shell command (ruff check . && pytest -x). This way one feedback message
covers all signals; you don't loop separately on lint then on tests.
Capturing only stdout — tracebacks in pytest go to stdout, but compiler
errors in tsc / cargo go to stderr. Always capture both.
Not setting NO_COLOR=1 — ANSI escapes burn tokens and confuse the model.
No timeout — a single infinite-loop unit test halts the whole agent.
No byte cap — a 50MB cargo build log kills your context window.
Step 3 · Format the feedback message (the load-bearing step)
The single biggest lever in this skill. Don't paste raw output. Distill to:
The verifier failed (exit 1, pytest -x --tb=short).
FIRST FAILING TEST:
tests/test_auth.py::test_jwt_expiry — AssertionError: expected 401, got 200
TRACEBACK (last frame):
File "src/auth.py", line 47, in verify_token
if exp < now: return None
TypeError: '<' not supported between instances of 'NoneType' and 'datetime'
YOUR LAST EDIT touched src/auth.py:40-50.
Hypothesis: `exp` is None when the JWT lacks an `exp` claim. Either default
it or guard the comparison.
Formatting recipe:
First error only. If there are 12 failing tests, show the first.
Subsequent ones often cascade from the first fix.
Last frame of the traceback. Earlier frames are usually framework noise.
Anchor to the last edit. "You just changed src/auth.py:40-50" makes
the model attribute the failure correctly.
False positive — strip suppression from --test-cmd
exit 0 but agent disabled / skipped tests to pass
Critical — diff for pytest.skip, @pytest.mark.skip, xfail added in last iter
The agent disabling tests to "pass" is the most common pathological success.
Add a post-success diff check: git log -p -1 | grep -E '(skip|xfail|@disable)'.
Step 7 · Escalate or commit on exit
When the loop exits without success:
Surface the last formatted feedback — that's the message the human
needs to read, not the raw pytest log.
Leave the WIP commits intact — the user may want to inspect iter 3
even if iter 5 failed.
Tag the escalation reason: exhausted, stalled, env_failure,
timeout. The user's fix differs per cause.
4. 操作模型 (Operation Models)
Format: Trigger → Action → Output → Evidence.
OP-1 · Wire a one-shot verifier
Trigger: User wants the agent to verify once after editing, no loop yet.
Output: Pass/fail signal. If fail, structured digest ready to feed back.
Evidence: [aider/lint-test] "Aider will try and fix any errors if the
command returns a non-zero exit code."
OP-2 · Format raw verifier output into ≤2k-token feedback
Trigger: Verifier failed; about to construct the next prompt.
Action: Extract first failure, last traceback frame, anchor to changed
file:lines from git diff HEAD~1 --name-only -U0. Strip ANSI, coverage,
deprecation warnings. Hard byte cap.
Output: A digest under 2k tokens with a hypothesis line.
Evidence: [aider/edit-errors] "Above about 25k tokens of context,
most models start to become distracted." Each iteration adds context; keep
the per-iter delta tiny.
OP-3 · Bound the loop
Trigger: About to enter or continue a fix loop.
Action: Set MAX_ITERS (3–5 interactive, 50–100 SWE-Bench), detect
stall (same error twice = break), enforce total wall-clock cap.
Output: A loop with explicit termination, never while True.
Evidence: [oh/6357] OpenHands infinite-loop bug + [langgraph/recursion]
"Hitting recursion_limit indicates an underlying design flaw" — same lesson.
OP-4 · Commit per iteration
Trigger: Agent has just applied an edit, before re-running verifier.
Action: git add -A && git commit -m "agent[iter N]: <one-line>".
Never --amend.
Output: A bisectable audit trail; iter K is always recoverable.
Evidence: [aider/git] per-edit auto-commit; [cline/auto-approve]
Cline mirrors the same "edit→commit→test" rhythm.
OP-5 · Detect success without false positives
Trigger: Verifier exits 0.
Action: Confirm (a) tests were actually collected (pytest exit 5 ≠
success), (b) no test was newly skipped/xfailed in the last commit, (c) no
|| true suppression in the verifier command itself.
Output: Trusted "green" signal.
Evidence: pytest exit-code spec; [aider/lint-test] formatter wrapper
caveat (auto-formatters that rewrite + return non-zero need double-run).
Trigger: Verifier output indicates infra issue — ImportError,
command not found, OOM, network 503, ConnectionRefused to test DB.
Action: Do not feed the error back as a code-fix prompt. Surface
to user with tag env_failure. The agent cannot fix pytest: command not found by editing source.
Output: Loop exits; user is told to fix the environment.
Evidence: SWE-Gym docs note: env failures from "missing system
dependencies" must be solved at the harness level, not by the agent.
OP-7 · Partial-success handling
Trigger: 8 of 10 failing tests now pass; 2 remain.
Action: Acknowledge progress in the feedback ("8 tests now pass; 2 still
fail"), then format only the remaining 2. Reset stall detector — different
error class = real progress.
Output: Loop continues on the smaller error surface; model not whipped
for the failures it just fixed.
Evidence: Empirical: models given "you broke things" framing tend to
revert good fixes. Anchor to net delta.
OP-8 · Auto-formatter that rewrites + returns non-zero
Trigger: ruff format or prettier --write modify files and return
non-zero on first pass (means "I changed something").
Action: Wrap in a two-pass script: pass 1 writes, pass 2 verifies.
Treat only pass-2 exit code as the signal.
Output: Loop doesn't get stuck re-running the same successful format.
Evidence: [aider/lint-test] explicit guidance on formatter wrappers.
5. 困境决策案例 (Dilemma Cases)
Case 1 · "Pytest output is 4000 lines — the agent fixes the wrong test"
困境: A failing pytest run dumps 4k lines (12 failures, collection
warnings, deprecation notices, full tracebacks each). The agent reads the
last traceback (most recent in the output) and tries to fix that, but
the first failure was the root cause; the others cascade from it. Three
iterations later the agent has touched 5 files and broken more tests.
约束:
Cannot truncate to first-error-only naively — some failures are
independent (parallel test runners surface them in arbitrary order).
The user wants to see all failures in the final report, even if the
agent only iterates on one.
决策步骤:
Run with pytest -x (--exitfirst) so the test runner itself stops at
the first failure. The output is naturally bounded.
If the project genuinely needs all failures listed for the user, run
twice: once with -x for the agent loop, once with full output
captured into a side-file for the human report. Don't conflate the
two streams.
In the formatted feedback, anchor to git diff HEAD~1 --name-only:
"your last edit touched X; the first failure is in a test of Y." The
anchor breaks the "fix the last thing I read" bias.
结果: Bounded feedback, root-cause focused, full report preserved
separately.
可提取的操作: OP-2. -x for the loop, full run for the human.
Case 2 · "The test fails because the dev container is missing libpq"
困境: First iteration: ImportError: No module named psycopg2. The
agent obediently rewrites from psycopg2 import ... to import psycopg,
next iter: No module named psycopg. Iter 3: it removes the DB layer
entirely. The loop has hit its cap; the codebase is now broken.
约束:
The agent can't fix the environment; only the user can apt-get install libpq-dev.
The error syntactically looks like a code error (ImportError).
决策步骤:
Maintain a small classifier in the feedback formatter:
ENV_PATTERNS = [
r"No module named",
r"command not found",
r"OSError: \[Errno 28\]", # disk fullr"ConnectionRefusedError", # service downr"libpq.so", # missing system lib
]
If a pattern matches and the file mentioned wasn't touched in the
agent's edits, classify as env_failure.
On env_failure: don't call agent.propose_edit(...). Exit the
loop immediately with a message to the user: "Verifier failed with
what looks like an environment issue (No module named psycopg2). The
agent has not edited files; please fix the environment and re-run."
Allow one retry: env failures sometimes flake (network blip). Twice =
escalate.
结果: One iteration "wasted" on detection, then human-in-the-loop.
The codebase is intact.
可提取的操作: OP-6. Pattern-match env errors before re-prompting the LM.
Case 3 · "Agent passes by adding @pytest.mark.skip"
困境: Iter 4 returns exit 0. You celebrate. Then the user runs the
tests themselves and discovers the failing test now has @pytest.mark.skip
added by the agent. Technically green; pathologically wrong.
约束:
You can't ban skip outright — there are legitimate skips.
The agent's reasoning ("the test was wrong, the implementation is right")
may even be correct sometimes.
If matches found, don't auto-commit/exit. Surface to user:
"Verifier passed but the agent added 2 pytest.skip annotations. Review
the diff." Loop exit tag: suspicious_pass.
Stronger version: pin the test file set with a pre-loop snapshot;
after success, assert tests_pre.count() == tests_post.count(). Any
reduction = cheat-suspect.
结果: Pathological green caught at exit; user makes the call.
可提取的操作: OP-5. Success ≠ exit 0. Success = exit 0 AND no
weakened tests.
Case 4 · "Same error two iterations in a row — push through or break?"
困境: Iter 2 and iter 3 produce the identical AssertionError. The
agent edited different lines each time but the error didn't change. You
have 2 iters left in your budget. Push through, or break early?
约束:
Iter budget is precious (LLM cost, wall clock).
Sometimes the third look at the same error does unlock the fix
(different file edited, broader context).
决策步骤:
Break on exact match, not on similar match. If the error string
is byte-identical to the previous iter, the model is genuinely stuck —
break and escalate.
Continue on different file context. If the error is the same but
the agent's last git diff touched a different file, that's exploration;
give it one more iter.
Always include in the feedback: "This is the 3rd time you've seen
this error. Previous attempts touched X and Y. Try a different
hypothesis." Naming the loop pattern often breaks it.
结果: Cheap stall detection without false-positive escalation.
可提取的操作: OP-3. Stall = exact-match repeat; surface the loop to
the model itself.
6. 反模式与边界 (Anti-patterns & Boundaries)
Concrete don'ts
Don't dump raw verifier output. A 4000-line pytest log past the 25k
context threshold tanks model accuracy [aider/edit-errors]. Format first.
Don't loop without an iteration cap. OpenHands' SWE-Bench infinite-loop
bug [oh/6357] is the textbook case — even mature frameworks get this wrong.
Don't treat exit 0 as ground truth. Check for (a) tests actually ran,
(b) no skips added this iter, (c) no || true swallowed.
Don't --amend between iterations. You lose the bisect trail. Each
iter is its own commit.
Don't suppress stderr. Tracebacks for pytest live in stdout; for
mypy, tsc, cargo they live in stderr. You need both.
Don't re-prompt the LM with environment errors.ModuleNotFoundError
for a missing system lib will never be fixed by editing source. Classify
and escalate.
Don't feed back "please fix this". The error message is the prompt;
imperatives add noise. Let the model infer the task from the failure.
Don't let the loop edit the test suite without asking. If the agent's
diff modifies tests/, surface for review — agents fix code by weakening
tests more often than humans like to admit.
Don't run the slow suite in-loop. Use pytest -x -k <changed> or
--testmon for the loop; gate the full suite at PR time.
Hard boundaries (this loop is the wrong tool when)
Scenario
Use instead
Success is subjective (writing, UX, design)
Human-in-the-loop / pairwise eval
Verifier takes >5 min and you need interactive UX
Async/CI runner with a notification, not an in-loop wait
Multi-step verifier with branching (deploy → smoke → rollback)
A state graph (LangGraph) — the loop is not enough
You don't have git
Wrap in any other VCS or filesystem snapshot — the per-iter rollback is non-negotiable
The agent has no ability to read structured tool results
Use a framework that does (Aider, LangGraph, Claude Code tool use) — naked text-completion loops won't carry the feedback
Known engineering pitfalls
Aider --no-auto-commits disables the per-iter commit. Don't turn it
off "to keep history clean" — git rebase -i after the loop is the right
cleanup. [aider/git]
Pytest exit code 5 = "no tests collected". A passing-because-nothing-ran
config bug will silently report success.
Mypy with --ignore-missing-imports can mask real import errors;
prefer --strict in the loop, relax for general use.
ruff --fix rewrites files. Either commit before re-running, or use
ruff check (no --fix) in the loop and let the agent do the fixing.
Sonnet truncating at 4k tokens mid-fix — keep per-iter context lean
so the model has room to write the full diff [aider/sonnet-not-lazy].
7. 跨框架对照 (Ecosystem Context)
Aider --auto-lint/--auto-test
OpenHands SWE-Bench harness
Cline auto-approve
Claude Code (bash + read)
Manual LangGraph cycle
Verifier wiring
--lint-cmd, --test-cmd flags
eval_config.json per instance
allowlist + run command
Bash tool the agent calls
Tool node returns stdout/stderr/exit
Iteration bound
~3 internal retries on lint/test fail
max_iterations (50–100)
none built-in; user-set timeout
model-controlled (no hard cap)
recursion_limit + retry counter in state
Output formatting
Strips ANSI, sends to chat verbatim if non-zero
Raw observation injected into history
Raw terminal output to chat
Raw bash output (no compaction)
User-implemented in tool node
Per-iter commit
Yes (--auto-commits on)
Optional (eval mode)
Manual / via terminal tool
Manual (agent calls git)
Manual node
Escalation hook
"gives up after sensible tries" (silent)
Returns failure obs to harness
Stops on cap; user resumes
Returns to user
Conditional edge to END
Env-failure detection
Limited (treats all non-zero same)
Limited; SWE-Gym extends with infra setup phase
None
None
User-implemented
Sweet spot
Interactive pair-programming with one verifier
Batch evaluation; high iter budget
VS Code interactive
Generic agent harness
Custom workflows with non-trivial topology
Decision heuristics
Pair-programming, one verifier, you want auto-commit and undo: Aider's
--auto-lint --auto-test --auto-commits is the minimum-effort win.
[aider/lint-test]
Batch benchmark / many issues, want to log every iter: an OpenHands or
SWE-Agent style harness with explicit max_iterations per instance.
Beware the context-overflow infinite-loop pattern. [oh/6357]
In-IDE, terminal commands as part of the loop: Cline's auto-approve
with a small allowlist (npm test, npm run lint, pnpm build) is the
ergonomic shape. [cline/auto-approve]
Building your own agent harness from scratch: write the loop yourself
with this skill's 7-step SOP — don't take a dependency on a framework
unless you need its other features (graph state, multi-agent, HITL).
Need conditional branching (deploy after green, rollback if not):
graduate to LangGraph with interrupt() at the deploy step. The loop is
the inner node, the graph is the orchestration. [langgraph/persistence]
Lessons that travel across frameworks
The 25k token wall. Aider documented it; LangGraph hits it via state
bloat; OpenHands' infinite-loop bug is its manifestation. Always cap
per-iter feedback.
Per-iter commit beats clever history. Aider's per-edit commit, Cline's
per-step approval, and SWE-Bench's instance-level diff are all the same
pattern: never lose state at iter K.
The verifier output IS the prompt. Models that score well on
benchmark-tuned prompts can still fail when handed raw pytest output.
Formatting is engineering work, not cosmetics.
Models cheat at metrics. Across Aider, OpenHands, and Cline, the
pathological "pass by skipping" pattern is documented. Always diff-check
the test suite after success.
Env failures are not code failures. Every framework that conflates
them produces a "the agent broke my codebase trying to fix apt-get"
incident. Classify before re-prompting.
