Agent Skills

This repository is a home for many named agent skills.

Each skill lives in its own flat directory under skills/ and should include a SKILL.md file plus any optional agents/, scripts/, references/, or assets/ needed to make the skill reliable and reusable.

Skills are kept flat: a skill never contains another skill's SKILL.md nested inside it. Agents such as Claude Code load skills from a flat skills directory and do not support nested skills, so a related set of actions (for example the labnb-* family) ships as several top-level skill directories rather than one skill with sub-skill folders. This keeps the whole skills/ tree installable into Claude Code, Codex, and other agents.

Skills

The list below is kept in alphabetical order by skill name.

Skill	Description
duct	Wrap any command with con/duct to capture wall-clock time, CPU, and memory usage as structured logs, so agents and reviewers can inspect what a run actually consumed.
kya	Govern and review agents with veldt-kya (Know Your Agents) — risk-score, consensus-judge, and drift-check an agent, emit compliance evidence, and write a governance verdict that the labnb loop can break on.
labnb	Create and maintain a concurrency-safe global lab notebook outside project roots, with startup summaries of related prior work, first-class idea capture and promotion, isolated experiment workspaces, focused companion skills, and append-only indexing across projects, investigations, and tasks.
labnb-idea	Record a promising but not-yet-implemented experiment idea in the shared lab notebook index.
labnb-promote	Promote a lab notebook idea into a concrete experiment with explicit budgets, source links, and provenance.
labnb-resume	Summarize prior ideas and experiments for a project slug, then choose whether to resume, promote, branch, or start new work.
labnb-run	Create and run a concrete lab notebook experiment with isolated workspace, explicit budgets, and iterative logging.
structsense-skills	StructSense Skills transforms unstructured text and PDFs into validated, ontology-grounded structured JSON using a model-agnostic extraction pipeline.

Installing Into An Agent

Use scripts/install_skills.py to copy the flat skill directories into an agent's skills directory:

python scripts/install_skills.py --list                  # show available skills
python scripts/install_skills.py --agent claude           # -> ~/.claude/skills
python scripts/install_skills.py --agent codex            # -> ~/.agents/skills
python scripts/install_skills.py --agent codex --scope project   # -> ./.agents/skills
python scripts/install_skills.py --agent codex --skills labnb duct
python scripts/install_skills.py --dest /path/to/skills   # any other agent

Skill directories follow the cross-agent Agent Skills format: a flat directory with a SKILL.md (with name and description frontmatter). The optional agents/openai.yaml adds Codex-specific UI metadata and invocation policy and is ignored by agents that do not use it. Default install locations:

• Claude Code: ~/.claude/skills/ (user) or <project>/.claude/skills/ (project).
• Codex: ~/.agents/skills/ (user) or <repo>/.agents/skills/ (project), the Agent Skills open-standard location.

The installer refuses to install a skill that contains a nested SKILL.md, which guards against re-introducing the structure that some agents cannot load.

Continuous Integration

.github/workflows/ci.yml runs on every push and pull request, with no human interaction or API keys required:

• Unit tests: uv run python -m unittest discover -s tests.
• Validate skill format: scripts/validate_skills.py checks every skill against the Agent Skills format (flat directory, required name/description frontmatter, matching name, no nested skills, parseable agents/openai.yaml, and that referenced scripts exist) and dry-runs the installer.
• duct skill smoke test: installs con-duct, captures a real command with duct, and summarizes the run with the skill's helper.

Utilities

• scripts/install_skills.py: install the flat skills in this repository into an AI coding agent's skills directory (--agent claude/codex, --scope user/project, or an explicit --dest), validating that no skill contains nested skills.
• scripts/validate_skills.py: deterministically validate that every skill is loadable by SKILL.md-based agents; used by CI.
• scripts/launch_agent_container.py: launch codex or claude inside a tightly-scoped Docker or Apptainer container using a reusable TOML config, including explicit auth mounts when credentials live outside the main agent state directory. See docs/agent-container-launcher.md.