cleanup-codebase
Reduce concepts, duplication, and ceremony in internal code while touching nearby code. Use when working an existing path and you spot dead fields, redundant wrappers, or speculative abstractions; distinct from refactor-break-bw-compat (internal hygiene, not public API removal).
What this skill does
# Cleanup codebase — local simplicity, ruthlessly applied Code rots in two directions: outward (drift from the original design) and downward (accretion of dead state, redundant indirection, speculative ceremony). This skill addresses the second. The thesis is local: you are already in nearby code for some other reason; while you are there, remove what does not earn its keep. **Modern insight (2025)**: Kent Beck's *Tidy First?* thesis and Casey Muratori's dataflow-first design heuristic both converge on the same conclusion that this skill's compress-side operations operationalize — small, frequent, atomic cleanups embedded in the active commit stream beat scheduled "cleanup PRs" by a wide margin. Scheduled cleanups bundle unrelated concerns and become unreviewable; embedded cleanups stay reviewable because their scope is the file already in your hands. See [dead-fields](references/dead-fields.md) for examples of dead struct fields, props, and class members. See [redundant-wrappers](references/redundant-wrappers.md) for examples of single-line passthrough functions that should be inlined. See [dead-config](references/dead-config.md) for stale feature flags, environment variables, and dead config branches. --- ## Mandates, not suggestions These are mandates, not suggestions. Internalize them as rules; do not paraphrase. ### 1. Minimize concepts, duplication, and ceremony. Every concept the reader has to hold in their head has a cost. Every duplicated piece of logic has two places to drift apart. Every ceremonial wrapper, factory, or builder that does not protect a real boundary is a tax on every future reader. Reducing concepts is not the same as reducing lines — it is reducing the number of distinct things a reader has to track. ### 2. One real owner per contract. No mirroring, no wrappers unless they remove real coupling. A "contract" is the truth about what some piece of state means or what some operation does. It must have exactly one owner. Mirroring (two structures holding the same field; two services maintaining the same cached state) is a guaranteed-future-bug pattern. Wrappers are coupling-removal tools — if a wrapper just renames or forwards without removing coupling, it is ceremony. ### 3. Local simplicity over speculative abstraction. Add indirection only when it removes real coupling or protects a real boundary. "Speculative" means "I might need this later." You will not need it later in the form you imagine now, and the indirection you add now will make the actual future change harder. Indirection earns its keep when it removes coupling that *currently* exists, or when it protects a *current* boundary (process, untrusted-input, async/sync seam). Otherwise it is dead-weight ceremony. ### 4. When caller and callee are both local with no real boundary, change both directly. A "local change" that has to ripple through three wrapper layers is not local — it is coupled. If `caller` and `callee` are both yours, both in the same module, with no API boundary between them, refactoring them in lockstep is correct. Resist the urge to "preserve the interface" of internal functions; an internal function's interface is whoever calls it. ### 5. Remove dead code, fields, config, and stale state while touching nearby code. Dead code is not free. It misleads readers about what the system does, it survives grep searches and pulls attention, it tricks reviewers into preserving it "just in case." While you are in nearby code for some other reason, remove what is dead. Make the deletion its own atomic commit so reviewers see exactly what disappeared. --- ## What "real boundary" means Indirection earns its keep at: **public API surfaces**, **process/network seams** (RPC, HTTP, queues), **untrusted-input boundaries**, **async/sync seams**, **runtime seams** (FFI, WASM, JNI), and **test/production seams** where mocks legitimately substitute. A swappable-implementation contract counts only when >1 real impl ships today — not "might exist later." Not boundaries: internal modules in the same crate/package, helpers in the same file, cross-module calls without a constraint that prevents co-change. --- ## When to Apply - You are editing `foo.py` for an unrelated feature; while reading the file, you notice a dead field - A refactor commit just ripped out a code path; the leftover wrapper, dead branch, or stale flag should leave with it - Reviewing a PR diff that adds an unnecessary wrapper or duplicates state; flag and request inline simplification - Onboarding to a codebase — surface candidates for the original author to confirm dead ## When NOT to Apply - **Standalone "cleanup sweep" PRs** — these mix unrelated changes, become unreviewable, and conflict with the `<git>` charter's "one concern per commit" rule. Solution: `git move --fixup` to embed the cleanup as an atomic commit alongside the active change. - **Files you are not otherwise touching** — opportunistic edits become unreviewable noise; the cleanup must ride alongside work that justifies you being in that file. - **Speculative removals you cannot prove are safe** — if you cannot grep-confirm that nothing reads a field, do not delete it; investigate first. - **Public API surfaces** — that is `refactor-break-bw-compat`'s territory and needs a migration plan. --- ## Decision rubric | Pattern | Action | Notes | |---------|--------|-------| | Wrapper that adds nothing but a rename | Inline, then delete the wrapper | Renames are not abstractions | | Field set in constructor, never read after | Delete the field and its assignment | Grep all consumers first | | Config flag where both branches are dead (always-on or always-off) | Delete the flag, keep the winning path | Often legacy migration debt | | Adapter between two structurally equivalent local types | Collapse to one type | Different names ≠ different concepts | | Helper used in 3+ places that genuinely names a shared concept | Keep | Real reuse, real naming | | Helper used in 1 place that wraps a 2-line body | Inline | The wrapper is overhead | | State mirrored across two services / two structs | Pick one owner; the other reads from it | Mirroring is the bug | | Comment that contradicts the code | Update or delete the comment | Stale comments mislead | | `TODO` from > 6 months ago | Open issue or delete | Indefinite TODOs are noise | --- ## Workflow 1. **Identify candidate** — while in the file for another reason, spot dead/redundant code. 2. **Confirm dead** — `git --no-pager grep -n` (or `ast-grep`) to verify no consumers; check tests, docs, configs, error messages. 3. **Check coupling effects** — does removal break the build? Force a refactor of the only consumer? That is a separate decision; record it. 4. **Verify against `~/.claude/claude/system-prompt-baseline.md` `<git>` charter** — cleanup is its own atomic commit. If it is mixed in with behavior change, split via `git move --fixup` / `git split`. 5. **Apply the deletion** — `git rip` the file or precise `Edit` for partial removal; never comment-out. 6. **Verify** — build, tests, type-check still pass. If a test was the only consumer of the dead code, that test was probably testing the dead code; see `tests-purge-unneeded`. 7. **Search for ghosts** — string references in docs, error messages, config keys, env vars, log lines that mention the removed concept. --- ## Constitutional Rules (Non-Negotiable) 1. **Never bundle cleanup with behavior change in one commit** — split via `git move --fixup` so each commit has exactly one concern. Cleanup commits ride alongside behavior commits in the same PR; that is fine and encouraged. 2. **Never add an abstraction during cleanup** — cleanup removes; if a new abstraction is genuinely warranted, that is a *separate* commit with its own justification. 3. **Never extend cleanup beyond files already touched by the active change** — opportunistic sweeps across the codebase are out of scope; they belong in scheduled refactor work th
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.