Results for “fix”

Automatically detects project state and runs an 8-phase pipeline to plan, build, test, lint, fix bugs, and document code across multiple AI coding agents.

Score a UI file's design quality 0-100 against StyleSeed's design language with per-category breakdown, worst offenders, and prioritized fix list.

Detects and fixes common MSBuild anti-patterns in .csproj, .vbproj, .fsproj, .props, .targets, and .proj files, covering Exec misuse, unquoted conditions, hardcoded paths, and more.

Use this skill for CI log analysis, local reproduction, root cause isolation, targeted fixes, rerun strategy. Trigger when the task involves programming work related to CI Failure Triage, implementation, audits, debugging, strategy, or validation.

Evidence-first repo execution workflow for ECC. Use when the user wants a command run, a repo checked, a CI failure debugged, or a narrow fix pushed with exact proof of what was executed and verified.

Analyzes past agent sessions to identify recurring failures and skill gaps, then automatically creates or patches skills and runs self-evolution to fix them, tracking improvement over time.

Shape, bet, and build products using the 37signals philosophy from Getting Real, Rework, and Shape Up — fix time, flex scope, and ship with small autonomous teams.

Use before touching any code when user gives a task, spec, or feature request. Forces writing a plan before implementation. Examples: 'implement X', 'add feature Y', 'fix Z and also update A', any multi-step task that would touch 2+ files.

Repo housekeeping: sync READMEs to match current architecture, remove dead files (logs, screenshots, deprecated code, build artifacts), update npm/pip/cargo dependencies to latest. Use when asked to: "housekeep", "clean up repo", "update README", "update dependencies", "fix vulnerabilities", "remove dead code", "tidy.

Use when about to claim work is complete, fixed, or passing, before committing, before reporting a task done, or before telling the evaluator the batch is ready. Requires running the verification command and reading its output in this turn before any success claim; evidence before assertions always.

Use when a task is well-defined, has an automatic way to verify success (tests, linter, build), and is tedious or large enough to benefit from unattended iteration - e.g. "get all tests passing", "implement this spec end to end", "fix every lint error". Not for one-shot changes or work that needs human judgment mid-stream.

In-session iterative loop — keeps Claude running in the current session until a task is done. Unlike /loop (which spawns background workers), /iloop stays in the current session and re-prompts each iteration via Stop hook. Use for: 'keep fixing until tests pass', 'iterate until this feature works', autonomous debugging. Triggers on: '/iloop', 'in-session loop', 'keep iterating', 'loop until done'.

Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"

Register a deferred decision in the debt registry. Trigger by judgment, not a marker scan, whenever a future reader would ask "why this way?": an unmade decision, stub, loosened type, bypassed check, swallowed error, a default picked "for now", or a TODO/FIXME/HACK/XXX marker. Trigger immediately whenever you defer work, or when the user invokes $add. Over-register freely; the developer drops with "drop A", "drop A,C", or "drop all".

Score any piece of writing for AI-generation tells and produce a weighted 0-100 scorecard with flagged evidence and ranked fixes. Use this skill whenever the user asks "does this sound AI-written", "run this through the AI detector", "score this writing", "check this for AI tells", "would this pass as human", "humanize check", or wants any article, blog post, email, or copy audited for AI patterns before publishing. Also use it when the user pastes or points to text and asks how it reads, whether it's too "ChatGPT-ish", or wants a QA pass on generated content. Works on pasted text, files, and URLs.