pushback: how often do you correct your AI coding agent?

Results · Quick start · How it works · Rules · Preference pairs · Privacy · Method

pushback is a command-line tool that reads your Claude Code transcripts and shows what you use your AI coding agent for, how often you correct it on each kind of work (code, writing, media, research, ops, meta), and how often a correction needs correcting again. It drafts CLAUDE.md rules from the corrections you keep repeating, shows which of your existing rules keep getting broken, and exports your corrections as preference pairs (prompt / chosen / rejected). A hand-audit step tells you how far to trust every number.

pip install pushback

Results

The author's own 1,633 messages to Claude Code:

task	messages	share of use	corrections	rate	95% CI	corrected again
writing	429	26%	106	24.7%	20.9%–29.0%	41% (41/99)
code	340	21%	20	5.9%	3.8%–8.9%	11% (2/18)
meta	297	18%	14	4.7%	2.8%–7.8%	36% (5/14)
ops	265	16%	12	4.5%	2.6%–7.7%	8% (1/12)
research	188	12%	13	6.9%	4.1%–11.5%	8% (1/13)
media	114	7%	49	43.0%	34.3%–52.2%	44% (20/45)

Most corrected topics (at least 5 messages each):

topic	messages	corrections	rate	corrected again
writing / social-post	135	47	35%	23/45
writing / client-message	144	26	18%	7/23
media / image	47	24	51%	10/21
media / video	36	12	33%	6/12
writing / proposal-report	26	11	42%	5/11
writing / video-script	36	10	28%	3/9
code / frontend	31	6	19%	1/6
code / debugging	27	5	19%	1/4

• Against 91 hand-checked messages the correction labels had precision 0.97 and recall 0.80. Topic labels were spot-checked, not audited.
• 1 in 3 corrections needed a second one. 70 of 201 fixes got corrected again (35%), 41% on writing and 44% on images.
• 3 rules the author had already written kept getting broken, with 6 to 8 corrections each.

That's one person's logs. Run it on yours.

Quick start

pip install pushback

pushback extract            # reads ~/.claude/projects, writes ./pushback-data/
pushback label              # labels each message with Claude (resumable)
pushback audit              # hand-check 40 messages
pushback report --markdown  # the table, with the audit folded in
pushback rules              # draft CLAUDE.md rules from your recurring corrections
pushback export             # your corrections as prompt/chosen/rejected pairs

label uses the Anthropic SDK, so it picks up ANTHROPIC_API_KEY or an ant auth login profile. It defaults to claude-opus-5 at low effort; change it with --model. To send through a gateway, set ANTHROPIC_BASE_URL.

command	what it does	leaves your machine?
extract	pulls the messages you typed, plus full agent turns, out of transcripts	no
label	tags each message with a task, a topic and whether it's a correction	yes: to the API after asking, or through Claude Code with --prompt-file
audit	shows you a stratified sample to judge by hand	no
report	what you use the agent for, how often you correct it per task and per topic (frontend, cli-tool, social-post, ...), how often a fix gets corrected again, with 95% intervals	no
rules	groups recurring corrections into CLAUDE.md rules	yes, or no with --prompt-file
export	writes preference pairs as JSONL, with secrets scrubbed	no

How it works

Every message gets a task (code, writing, media, research, ops, meta) and a topic from a fixed list for that task, so results stay comparable between people:

task	topics
code	frontend, backend-api, cli-tool, database, tests, auth-security, integrations, data-ml, scripts-automation, agent-prompts, debugging, pr-workflow
writing	social-post, client-message, email, docs-readme, video-script, proposal-report, spec-plan
media	image, video, diagram, ui-design
research	benchmark-experiment, data-analysis, market-leads, paper-reading, trading-analysis
ops	deploy-hosting, git-github, accounts-credentials, config-settings, browser-automation, ci
meta	planning, memory-context, advice, status-check

Anything that fits none of them is other.

A correction is a message where you reject, fix or redirect something the agent just did, said, wrote or proposed. New tasks, answers to its questions, picking between its options, approvals and pasted logs don't count. The full rubric is in src/pushback/prompt.py. If you change it, run audit again.

Rejected tool calls and interrupts carry no text, so they're counted separately and grouped by the action you stopped (a code edit, a shell command, and so on).

Turn recurring corrections into CLAUDE.md rules

pushback rules                                  # drafts rules into pushback-data/rules.md
pushback rules --existing CLAUDE.md notes/*.md  # also check against the rules you already have
pushback rules --prompt-file                    # no API key? writes the request to a file instead
pushback rules --from-response reply.json       # ...and reads Claude's answer back

The model groups corrections that share a cause and drafts one CLAUDE.md instruction per group. Support is counted from the correction ids it cites, not from its own numbers, and a rule needs at least 3 real corrections.

When you pass your existing rule files, the output splits in two:

• Rules you already have that keep getting broken. These are the useful ones. A rule that exists and still gets corrected isn't working: it's too vague, or the agent doesn't read it at the right moment.
• New rules to consider. Recurring corrections with no rule behind them.

Export your corrections as preference pairs

pushback export                                        # all pairs -> pushback-data/dpo.jsonl
pushback export --ctype writing_content tone_style     # the cleanest pairs
pushback export --minimal                              # only prompt/chosen/rejected (TRL DPO columns)

Each correction you made becomes one row:

field	what it holds
prompt	your message the agent was answering
rejected	the agent turn you corrected
chosen	the agent's reply to your correction, kept only if your next message wasn't another correction
feedback	the correction itself, for critique-and-revise formats

On the author's logs, 214 corrections gave 124 pairs. The biggest loss: 70 corrections (a third) had their fix corrected too, so there was no accepted answer to pair with.

Privacy

• extract, audit, report and export never leave your machine. report prints counts only.
• label and rules send each message, plus the end of the agent reply before it, to the model provider. If your logs contain client work, check that provider's data policy first. Both ask before they send anything. With --prompt-file nothing is sent by pushback; Claude Code reads the files instead.
• pushback-data/ holds your raw messages. It's in .gitignore. Keep it there.
• Your transcripts probably contain other people's information. Keep exports local unless every conversation in them is yours to share.

How the numbers are computed

• Rates per task come with Wilson 95% intervals.
• audit samples half from messages the model flagged and half from the rest. The report estimates the true count as flagged × precision + unflagged × miss rate, with a range built from both strata's intervals.
• A batch that fails stays unlabelled and is retried on the next run. It's never counted as "not a correction".
• Message ids are session:index, so re-running extract never shifts your labels.

Limits

• Claude Code transcripts only, for now. Codex and Cursor logs are not read yet.
• Task type is judged from the last agent reply and your message, not the whole session.
• The rubric was tuned on one person's logs.
• Labels aren't perfectly stable between runs. Two independent runs on the same 60 messages agreed on correction 93% of the time, on task 70%, and on topic 76% when the task matched. Disagreements sit at fuzzy edges (writing vs meta, code vs ops), mostly short "ok do that" messages. Treat small differences between topics as noise.

Contributing

Issues and pull requests are welcome, especially readers for other agents' transcript formats. See CONTRIBUTING.md.

License

MIT © Intikhab Azam

_{Keywords: Claude Code analytics · AI coding agent evaluation · correction rate · CLAUDE.md rules generator · agent transcripts · human feedback · preference pairs · DPO dataset · LLM evaluation · developer productivity}