Adds docs/optimize.md (what optimize scans, the three classes, the exact files --apply may touch plus undo, measured vs estimated, the health grade bands, the --yes CLAUDE.md guardrail), links it from the README waste section, and corrects the detector count in docs/architecture.md (14 -> 19).
4.8 KiB
optimize
codeburn optimize scans your Claude Code sessions and your ~/.claude/ setup, reports what is
costing tokens without earning them, and grades the setup A to F.
What it scans
- Session transcripts for the selected period: tool calls, per-call token usage, turn retries, per-session cost. This is where re-reads, junk directory reads, low read:edit ratios, warmup overhead, retries, and expensive or context-heavy sessions come from.
- Your configuration:
~/.claude.json, user and projectsettings.json/settings.local.json,.mcp.json,CLAUDE.md(including@-imports), and theskills/,agents/,commands/directories. This is where unused MCP servers, MCP deferral gaps, ghost skills/agents/commands, the bash output cap, and oversizedCLAUDE.mdfiles come from.
Nothing is written during a scan. Only --apply writes.
The three classes
Every finding carries a class, and both the CLI and the apps group by it:
| Class | Header | Meaning |
|---|---|---|
fix |
Fix now (apply-able) | CodeBurn can make this change for you: codeburn optimize --apply |
nudge |
Habits | Behavioural. Nothing to edit; the fix is how you drive the next session |
keep |
FYI | Informational. The cost may well be justified; decide for yourself |
A finding is fix only when a plan can actually be built for that instance. The same detector can
report a fix in one run and a nudge in another: mcp-deferral-off is appliable when the cause is
an ENABLE_TOOL_SEARCH override in a settings file, but manual when the cause is Vertex AI policy,
an outdated Claude Code, or an override that lives in your shell profile.
What --apply may write
--apply builds a plan per finding, shows you the exact files it will touch, and asks before
writing. --dry-run prints the plan and stops.
| Finding | File it edits |
|---|---|
unused-mcp, mcp-low-coverage |
~/.claude.json, project .mcp.json / settings.json (removes the server entry) |
mcp-project-scope |
moves a global server entry into the keeper project's .mcp.json |
mcp-deferral-off |
the settings file carrying the ENABLE_TOOL_SEARCH override |
mcp-alwaysload-hygiene |
the config files carrying "alwaysLoad": true |
mcp-defer-threshold |
the settings file carrying the auto:N threshold |
unused-agents, unused-skills, unused-commands |
moves the files into ~/.claude/<kind>/.archived/ |
bash-output-cap |
appends a marker block to ~/.zshrc / ~/.bashrc |
read-edit-ratio, build-folder-reads |
appends a marker block to the current project's CLAUDE.md |
Every write is backed up and journaled first:
codeburn act list # every change CodeBurn has made
codeburn act undo <id> # restore the original files
codeburn act undo --last
Undo refuses if a file changed after the apply, unless you pass --force.
The --yes CLAUDE.md guardrail
--apply --yes skips the prompt for every plan except CLAUDE.md rule blocks. Those land in the
CLAUDE.md of whatever directory you happen to be in, so a blanket --yes from an unrelated
directory would write advice into the wrong project. To apply one anyway, use the interactive picker
or name it explicitly:
codeburn optimize --apply --only read-edit-ratio
measured vs estimated
Each finding also carries a basis, printed next to its savings and summarised in the header as
N measured · M estimated:
- measured — the token number is summed from provider-counted usage on your own calls. Today
that is
context-heavy-sessionsandcost-outliers. - estimated — the token number comes from a model: a per-tool schema size, a per-line
CLAUDE.mdcost, an average read size, a recovery fraction applied to real turn tokens. A detector that mixes counted tokens with a model counts as estimated.
Sessions whose cost the provider never reported (Kiro, Cursor, some Cline sessions price from
modelled token counts) are kept out of the cost-outliers peer comparison, so a modelled cost is
never called an outlier against provider-reported ones. When a provider only ever estimates, the
comparison falls back to those sessions and the finding reports itself as estimated.
In --format json, summary.measuredSavingsUSD is the share of summary.potentialSavingsCostUSD
that comes from measured findings.
Reading the health grade
Health starts at 100 and loses points per finding: 15 for a high-impact one, 7 for medium, 3 for low. The total penalty is capped at 80, so a long tail of small findings cannot sink the score to zero on its own. The grade is a band over that score:
| Grade | Score |
|---|---|
| A | 90-100 |
| B | 75-89 |
| C | 55-74 |
| D | 30-54 |
| F | below 30 |
The grade rates your setup, not your spending: an expensive month with a clean configuration still scores an A.