borda/ai-rig
41 skills
$npx skills add borda/ai-rigSkillInstalls
agent-shimsDiagnose, inspect, install, update, or remove the complete Codex Rig user-agent shim roster. Use when the user invokes agent-shims with exactly one action—doctor, status, install, or remove—or asks toanalyseAnalyze GitHub issues, Pull Requests (PRs), Discussions, and repo vitality for an Open Source Software (OSS) project. For any specific item, casts a wide net — finds and lists all related open and cloauditFull-sweep quality audit of .claude/ config — cross-references, permissions, inventory drift, model tiers, docs freshness. Scope tokens select what to audit; --upgrade applies docs-sourced improvementbrainstormIterative brainstorming skill for turning fuzzy ideas into approved tree documents. Diverges into branches, deepens and prunes them over many rounds, saves a tree doc. Run breakdown on the tree to discalibrateCalibration testing for agents and skills. Generates synthetic problems with known outcomes (quasi-ground-truth), runs targets against them, measures recall, precision, confidence calibration — revealcode-remediateCodex-native code-remediation loop: triage/apply code-review findings, rerun checks, publish unresolved gaps with measurable gates; `$code-remediate #123 +review` remediates a PR from latest matching code-reviewTiered Codex-native multi-axis code review for local diffs or GitHub PRs, including `$code-review #123` (bare number = PR); uses mechanical scope gates, explicit specialist fan-out/substitutes, measurcreateInteractive outline co-creation for developer advocacy content — collects format, audience profile, story arc (Problem→Journey→Insight→Action), and voice/tone; detects out-of-scope requests (FAQs, comdebrief-codingRead local codemap telemetry logs and produce a diagnostic/usage report. Supports date filtering, session filtering, and optional anonymization before sharing. TRIGGER when: analyse recent codemap usadebugInvestigation-first debugging — gather evidence, form confirmed root-cause hypothesis, hand off to fix mode with diagnosis file. TRIGGER when: user reports a symptom or failing test with Python tracebdevelopMinimal codex-native develop loop. Use for implementation tasks with linear plan-build-verify flow and measurable quality gates.distillOne-time snapshot extracting patterns from work history and accumulated lessons, distills into concrete improvements — new agent/skill suggestions, memory pruning, consolidating lessons into rules/agefeatureTDD-first feature development — crystallise API as a demo test, drive implementation to pass it, run quality stack and progressive review loop. TRIGGER when: user asks to build new functionality, add fixReproduce-first bug resolution — capture bug in failing regression test, apply minimal fix, run quality stack and review loop. TRIGGER when: user reports a bug, regression, or unexpected behaviour in fortifySystematic ablation study runner. After research:run finds improvements, fortify identifies component candidates from git diff + diary, creates isolated git worktrees per ablation (main repo never modhumanizerStrip AI-writing tells from prose destined for humans — docs, PR/commit bodies, reports, release notes, blog posts, Slack/email drafts. Removes LLM-vocabulary clichés (delve, boasts, testament, undersintegrationManage codemap integration — 'check' audits installation health (scan-query reachable, index fresh, injection present), 'init' onboards codemap by discovering skills/agents, recommending injection sitinvestigateSystematic diagnosis for unknown failures — local environment, tool setup, CI vs local divergence, hook misbehavior, and runtime anomalies. Gathers signals broadly, ranks hypotheses, uses adversarial judgeResearch-supervisor review of program.md — validates experimental methodology (hypothesis clarity, measurement validity, control adequacy, scope, strategy fit), emits APPROVED / NEEDS-REVISION / BLOCKkaggleGenerate a Kaggle competition notebook as a Jupytext `# %%` Python script following the user's established ML research style: PTL for DNN training, best-fit tool selection, EDA→Baseline→Train→InferencmanageCreate, update, or delete agents, skills, rules, and hooks with full cross-reference propagation. Trivial edits (typos, small fixes ≤10 words) applied inline without agent; `.md` content-edits delegatoptimizeMinimal codex-native optimization loop. Use for metric-driven improvements with guardrails and measurable gates.planAnalysis-only planning — classify and scope a task without writing code; outputs a structured plan to .plans/active/. TRIGGER when: user wants to understand scope and risks before implementation; phraprofileSession clock-time analyzer. Reads the foundry plugin''s timings.jsonl and invocations.jsonl logs (written by task-log.js) and produces a per-session and per-skill wall-time breakdown — local-tool worquery-codeQuery the codemap structural index — module deps/rdeps/central/coupled/path, symbol-level source extraction, function call graph (fn-deps, fn-rdeps, fn-central, fn-blast). TRIGGER: "what depends on", refactorTest-first refactoring — audit coverage, add characterization tests, apply changes with safety net, run quality stack and review loop. TRIGGER when: user wants to restructure existing Python code withreleasePrepare release communication and check readiness. Main mode: notes with optional flags --changelog, --summary, --migration; range as v1->v2. Other modes: prepare (full pipeline: audit → all artifactsrename-refsAtomic rename of Python symbols or modules via the structural index — static callers, import sites, __all__ re-exports, Sphinx cross-refs; optional deprecated alias (--deprecate) or hard-delete (--remresearchMinimal codex-native research loop. Use for docs/papers/state-of-the-art scan with source-backed recommendations.resolveOSS maintainer fast-close workflow for GitHub PRs. Three phases: (1) PR intelligence — reads full thread, linked issues, PR body to synthesize contribution motivation and classify every comment into aretroPost-run retrospective: reads .experiments/ JSONL, computes Wilcoxon significance, detects dead iterations, flags suspicious jumps, generates next-hypothesis queue for --hypothesis flag.reviewMulti-agent code review of local Python files, directories, or the current git diff covering architecture, tests, performance, docs, lint, security, and API design. Scope: Python source files in localrunSustained metric-improvement loop with atomic commits, auto-rollback, and experiment logging. Iterates with specialist agents, commits atomically, auto-rolls back on regression. Accepts a program.md fscan-codebaseScan the Python codebase and build a structural JSON index (import graph + blast-radius metrics). TRIGGER when: user asks to build, refresh, or rebuild the codemap index; user mentions stale index, misessionSession parking lot — automatically parks diverging ideas and unanswered questions to project-scoped memory; /session resume shows pending items, /session archive closes them, /session summary gives asetupPost-install setup for foundry plugin. Run once after installing on a new machine, or after a plugin version upgrade to sync settings and symlinks. Merges statusLine, permissions.allow, enabledPluginssweepNon-interactive end-to-end pipeline — auto-configure program.md (accept defaults), run judge+refine loop (up to 3 iterations), then run the campaign. Single command from goal to result.syncDry-run-first Codex Rig installation drift and update workflow. Use to inspect the active plugin cache, refresh its GitHub marketplace, or reinstall the current plugin while coordinating separately matest-impactIdentify which tests need rerunning after a code change — traces static call graph (function-level) or import graph (module-level) to find affected test files, then emits a ready-to-run pytest commandtopicResearch State of the Art (SOTA) literature for an Artificial Intelligence / Machine Learning (AI/ML) topic, method, or architecture. Finds relevant papers, builds a comparison table, recommends the bverifyPaper-vs-code consistency audit. After research:scientist implements a method from a paper, verify the implementation matches paper claims across five dimensions — formula matching [F], hyperparameter