Borda

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.

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

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

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

Oferece um loop de melhoria contínua de métricas com commits atômicos, auto-rollback e registro de experimentos. Ele itera com agentes especialistas, aceita um caminho de arquivo program.md e suporta várias opções de linha de comando.

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.

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.

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

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

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

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

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.