Borda

Minimal codex-native develop loop. Use for implementation tasks with linear plan-build-verify flow and measurable quality gates.

Minimal codex-native optimization loop. Use for metric-driven improvements with guardrails and measurable gates.

Minimal codex-native audit loop. Use to scan codex configuration/workflow drift and emit ranked gaps with measurable gates.

Minimal codex-native management loop. Use to create, update, or remove Codex agents/skills/config entries with guardrails.

Minimal codex-native resolve loop. Use to apply review findings, rerun checks, and publish unresolved gaps with measurable gates.

Minimal codex-native investigation loop. Use for unknown failures and root-cause narrowing with measurable gates.

Minimal codex-native release loop. Use for SemVer-aware release readiness with measurable gates and artifact output.

Minimal codex-native analysis loop. Use for issue/PR/problem analysis before implementation with measurable gates.

Minimal codex-native research loop. Use for docs/papers/state-of-the-art scan with source-backed recommendations.

Query the codemap structural index — central, coupled, deps, rdeps, import path, symbol-level source extraction, and function-level call graph (fn-deps, fn-rdeps, fn-central, fn-blast). TRIGGER when: user asks about module relationships, dependency graph, callers/callees, or blast radius; phrases: "what depends on", "who calls", "imports of", "dependency graph", "blast radius of". SKIP: codemap in

Reproduce-first bug resolution — capture bug in failing regression test, apply minimal fix, run quality stack and review loop.

Test-first refactoring — audit coverage, add characterization tests, apply changes with safety net, run quality stack and review loop.

Minimal codex-native calibration loop. Use to detect leaks or major gaps across mirrored skills and agents with fixed checks.

Minimal codex-native review loop. Use for local diff review with measurable quality gates and a JSON artifact.

Minimal codex-native sync loop. Use to keep project and home Codex configs aligned and report drift.

Scan the Python codebase and build a structural JSON index (import graph + blast-radius metrics).

Investigation-first debugging — gather evidence, form confirmed root-cause hypothesis, hand off to fix mode with diagnosis file.

One-time snapshot extracting patterns from work history and accumulated lessons, distills into concrete improvements — new agent/skill suggestions, roster quality review, memory pruning, consolidating lessons into rules/agent updates, or performing bin/ extraction from /audit --efficiency candidates.

Manage codemap integration — 'check' audits installation health (scan-query reachable, index fresh, injection present), 'init' onboards codemap by discovering skills/agents, recommending injection sites, and wiring them in.

TDD-first feature development — crystallise API as a demo test, drive implementation to pass it, run quality stack and progressive review loop.

Analysis-only planning — classify and scope a task without writing code; outputs a structured plan to .plans/active/.

Interactive 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, comparison tables); surfaces conflicts between user brief and audience needs. Writes approved outline to .plans/content/<slug>-outline.md for foundry:creator to execute. Use when starting a blog post, Ma

Prepare 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 artifacts), audit (pre-release readiness: blockers, docs alignment, version consistency, CVEs), demo (story-telling release notebook in jupytext # %% format).

OSS 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 action items; (2) conflict resolution — checks out PR branch (fork-aware via gh pr checkout), merges BASE into it, resolves conflicts semantically using contributor's intent as priority lens; (3) imple

Multi-agent code review of GitHub Pull Requests (Python source, documentation (Markdown/RST), and CI/CD config PRs) covering architecture, tests, performance, docs, lint, security, and API design.

Full-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 improvements; --adversarial runs foundry:challenger + Codex adversarial review; --efficiency sweeps model tiers, token bloat, spawn patterns, boilerplate duplication, and bin/ extraction candidates (extraction p

Iterative 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 distill it into a spec via guided questions.

Calibration testing for agents and skills. Generates synthetic problems with known outcomes (quasi-ground-truth), runs targets against them, measures recall, precision, confidence calibration — reveals whether self-reported confidence scores track actual quality.

Paper-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 parity [H], eval protocol [E], notation consistency [N], and citation chain [C]. Reads paper (PDF path / arXiv URL / pasted text), maps claims to codebase, emits verification table with match status

Systematic diagnosis for unknown failures — local environment, tool setup, CI vs local divergence, hook misbehavior, and runtime anomalies. Gathers signals broadly, ranks hypotheses, uses adversarial review (Codex or foundry:challenger) for ambiguous cases, probes each, and reports root cause with a recommended next action. NOT for known code bugs (/develop:debug) or config quality (/foundry:audit

Create, 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 delegated to foundry:curator; code file edits (`.js`, `.py`, `.ts`) delegated to foundry:sw-engineer; large cross-ref fan-outs (> 3 files) also delegate. The parent orchestrates MEMORY.md, README, audit, cal

Session 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 a session digest TRIGGER when: user asks "what was I working on", "any pending items", "what''s in the parking lot", "remind me where we left off", "what did we defer"; resume intent clear from context

Research-supervisor review of program.md — validates experimental methodology (hypothesis clarity, measurement validity, control adequacy, scope, strategy fit), emits APPROVED / NEEDS-REVISION / BLOCKED verdict before expensive run loop.

Research 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 best implementation strategy for the current codebase, and optionally produces a phased implementation plan mapped to the codebase. Delegates deep analysis to the research:scientist agent and codebase

Post-install setup for foundry plugin. Merges statusLine, permissions.allow, and enabledPlugins into ~/.claude/settings.json; symlinks rules, TEAM_PROTOCOL.md, and skills into ~/.claude/.

Analyze 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 closed issues/PRs/discussions, explicitly flags duplicates. Summarizes long threads, extracts reproduction steps, and generates repo vitality stats. Uses gh Command Line Interface (CLI) for GitHub Applic

Systematic 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 modified), runs metric+guard in each worktree, ranks component importance, and optionally generates reviewer Q&A calibrated to a target venue.

Post-run retrospective: reads .experiments/ JSONL, computes Wilcoxon significance, detects dead iterations, flags suspicious jumps, generates next-hypothesis queue for --hypothesis flag.

Interactive wizard that scans the codebase, proposes a metric/guard/agent config, and writes a program.md run spec. Also runs cProfile on a file path to surface bottlenecks before prompting for optimization goal.

Non-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.

Realiza una revisión de código multiagente para archivos Python locales, directorios o diferencias de git, cubriendo arquitectura, pruebas, rendimiento, documentación, lint, seguridad y diseño de API. Su alcance son los archivos fuente de Python en el árbol de trabajo local, excluyendo proyectos sin archivos Python.

Ofrece un bucle de mejora continua de métricas con commits atómicos, auto-rollback y registro de experimentos. Itera con agentes especialistas, acepta una ruta de archivo program.md y soporta varias opciones de línea de comandos.