Claude Code maintenance skills

Seven Claude Code skills and one hook for keeping a project's knowledge and your Claude Code setup healthy over time: orient yourself when you come back to a project, keep its CLAUDE.md from bloating, recover anything useful left behind in old session transcripts, turn a helper script Claude wrote into a permanent tool of a skill, improve the development workflow when something slipped past it, compare one of your skills with somebody else's, review your setup against the models you run today, and save a session before it goes idle or grows too large.

Each skill is a self-contained directory (a SKILL.md). Claude can invoke one when its description matches what you are doing, or you can ask for it by name. The hook is a single script under hooks/.

When each one runs

Source: docs/maintenance-flow.drawio (editable in draw.io).

The skills

• project-status: cold-start orientation for a project. Gathers git state, open PRs, deploy health, local container status, OpenSpec change progress, unresolved review blockers, and pending items from project memory, then synthesizes a "what is the state of this project right now" report and suggests one next action. Read-only; it never mutates git, the deploy, or your files. Use it when you sit back down on a project after time away.
• split-claude-md: six-phase CLAUDE.md maintenance for a file that has grown too large. Phase 0 deletes what the repository itself answers. Phase A rotates old dated records into monthly archives with an index. Phase B consolidates recurring failure patterns. Phase C extracts stable reference sections to sibling files. Phase D keeps a rule in place and moves only its evidence out, which is the one cut that does not delete a rule. Phase E scopes file-triggered rules to .claude/rules/ so they leave the always-loaded set entirely. Ships survey.py, which measures the file and prints the candidates for phases 0, C, D and E, and rotate.py for the Phase A move. No-loss checks run around every cut: loss_check.py says where each rule sentence, heading, path and command of the old file went, pointers.py confirms every pointer still resolves, and recall_check.py asks a fresh session the same questions before and after the split. rotate.py --undo restores a run, and run_tests.py runs the skill's test suite. The skill body is a short driver; each phase's detail lives in split-claude-md/reference/, so a compaction cannot truncate it.
• salvage-session-archives: mine the Claude Code session-transcript leftover folders for a project, extract durable facts, decisions, and gotchas that never made it into the project's CLAUDE.md, cross-check them against what is already documented (duplicates and contradictions), append the genuinely new items under a "Salvaged from session archives" section, then delete the leftover folders. Recovers lost context and cleans up disk in one pass. It asks before deleting anything.
• save-script-to-skill: after a skill run where Claude wrote a helper script to finish the job, moves that script into the skill's scripts/ folder and rewrites the skill's SKILL.md so the next run executes the file instead of writing it again. Ships find_session_scripts.py, which reads the session transcript to find the skill invoked last and every script written or run after it. It makes the script reusable (arguments instead of session values, no secrets, clear exit codes), runs it once on the same input, and asks when more than one script could be the one.
• improve-dev-workflow: the continuous-improvement loop for the development process itself. When something reached you or a reviewer that a skill, hook, agent or rule should have caught, it runs Observe, Diagnose, Verify, Learn and Improve: it reads the session transcript, lists each incident with quoted evidence, looks through four lenses (time lost finding things, checks that already exist, tool cost, missing access to a log or service), sorts each cause into one of four classes (process, missing context, weak rule, unreliable code), and fixes it in the smallest place that loads by itself next time. An improvement is done only when each fix has a check that runs and goes red on the miss (a hook, a test, or a replay of the original incident), or is recorded as words-only with the reason. Each step ends on a "Done when" line. save-context step 7 (in the multi-phase-skills-framework pack) asks at the end of a session whether to run it, so close-session reaches it too.
• compare-skills: compares one of your skills, commands or agents with somebody else's on the same topic, feature by feature. It fetches theirs from a GitHub link or a path (with the files it depends on), reads both in full, writes one row per feature with an action for you to confirm (Enhance, Change, Keep mine, Nothing to do) and a note saying what the change would be, and saves the table as COMPARISON.md. A Google Sheet with a dropdown per row is optional. It recommends and never edits either skill.
• model-fit-review: reviews every skill, agent and command under ~/.claude for text written for older models that now costs a strong model: start gates that stop a run to ask, stale model names and dead tool references, the same sentence pasted many times, files long enough that a compaction drops the step that matters. A script measures the tree, a reader quotes what Anthropic and OpenAI recommend today from their own pages, seven parallel readers apply one rubric, every headline number is checked again, and the result is a report with your decisions first. It recommends and never edits. Use it when a new model family ships.

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