AI-Rig
Review Skills in Borda/AI-Rig, with license, maintenance context, and source paths.
- Skills
- 18
- Repository stars
- 25
- Identity status
- Source-linked
Provenance
Source and identity
- Profile type
- Repository
- Canonical name
- AI-Rig
- Public sources
- 1
- License context
- Apache-2.0
Source entries
Agent Skills from AI-Rig
Repository stars and maintenance signals provide context, but do not automatically become an individual Skill's quality score.
Borda/AI-Rig
analyse
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 Application Programming Interface (API) access. Complements oss:shepherd (requires `oss` plugin). NOT for
Borda/AI-Rig
audit
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 performed separately via /distill executables). Fix level chosen via always-fire follow-up gate after
Borda/AI-Rig
brainstorm
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.
Borda/AI-Rig
calibrate
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.
Borda/AI-Rig
code-remediate
Apply selected review fixes; bare PR targets use current online items, while PR +review adds the latest matching artifact.
Borda/AI-Rig
code-review
Close PRs at an evidence gate or review local diffs/PRs with specialists and JSON artifacts.
Borda/AI-Rig
distill
One-time snapshot extracting patterns from work history and accumulated lessons, distills into concrete improvements — new agent/skill suggestions, memory pruning, consolidating lessons into rules/agent updates, or performing bin/ extraction from /audit --efficiency candidates. Roster boundary analysis → /foundry:audit agents (Check 34).
Borda/AI-Rig
feature
TDD-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 a capability, or implement a feature in a Python project; phrases: "add X", "implement Y", "build Z feature", "create a new module for". SKIP when: bug fixes (use `/develop:fix`); refactoring without new behaviour (use `/develop:refactor`); non-Python projects; `.claude/` config changes (use `/found
Borda/AI-Rig
fix
Reproduce-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 Python code with a traceback, failing test, or issue number; phrases: "fix this bug", "repair X", "broken since Y", "test failing". SKIP when: CI-only failures without local traceback (use `/develop:debug` first); new features (use `/develop:feature`); `.claude/` config issues (use `/foundry:audit`)
Borda/AI-Rig
implement
Implement changes with a linear plan-build-verify workflow and measurable quality gates.
Borda/AI-Rig
investigate
Investigate code debugging and root-cause narrowing; use measurable gates before fixes.
Borda/AI-Rig
judge
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.
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Repository context
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