Chalk Labs
Need help deploying your-domain-name?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/sciknow-io-your-domain-name
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/your-domain-name
What's inside
- Skill name
your-domain-name- Description
- Brief description of what this skill does
- License
- MIT
- Instructions
- 53 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Agent Skills spec issues
- name: "your-domain-name" does not match folder "_template"
Files (8)
- README.md
- SKILL.md
- USAGE.md
- pipeline_template.py · pipelines/
- schema.tql
- eval_schema_template.yaml · schema/
- skill.yaml
- template.py
SKILL.md (first lines)
# Your Domain Name Skill Use this skill to [describe primary use case]. Claude acts as [describe Claude's role in this domain]. **When to use:** [Triggers: "ingest [item]", "analyze [item]", "show [entity]", ...] ## Prerequisites - TypeDB must be running: `make db-start` - Dependencies installed: `uv sync --all-extras` (from project root) - Schema loaded: run `make build-db` after adding your `schema.tql` ## Environment Variables - `TYPEDB_HOST`: TypeDB server (default: localhost) - `TYPEDB_PORT`: TypeDB port (default: 1729) - `TYPEDB_DATABASE`: Database name (default: alhazen_notebook) ## Quick Start ```bash uv run python .claude/skills/<your-domain>/<your-domain>.py list-entities ``` ## Schema Gap Recognition During sensemaking, if you encounter a concept, relationship, or entity type that has no place in the current TypeDB schema, that is a **schema gap** — a signal for schema evolution, not a failure. When you notice a schema gap: 1. Complete as much as possible with the current schema (partial knowledge > none) 2. Immediately file a gap issue: ```bash gh issue create --repo <owner/name> \ --title "Gap [moderate][entity-schema]: <one-sentence summary>" \ --body $'## What was missing\n<the concept you tried to represent>\n\n## What broke\n<which TypeDB entity/relation/attribute is absent>\n\n## Suggested fix\n<proposed TypeQL addition, or unknown>' \ --label "gap:open" ``` **Examples of schema gaps:** - A paper has a methodology section but there's no `methodology` attribute on `scilit-paper` - A job posting mentions a work arrangement type that isn't in the schema - A disease has a phenotype frequency that can't be attached to the current relation Use `--dry-run` first to review the issue before filing it. ## Command Output Pattern `uv run` emits a `VIRTUAL_ENV` warning to stderr. Always use `2>/dev/null` when piping output to a JSON parser — never `2>&1`, which merges the warning into stdout and breaks JSON parsing. **Before executing any commands, read `USAGE.md` in this directory for the complete command reference, workflows, and data model.**
From sciknow-io/skillful-alhazen, MIT.
More from sciknow-io/skillful-alhazen
- 0.554score
agentic-memory
sciknow-io
TypeDB-backed ontological memory with schema-driven retrieval.
SkillCFreshness: C★ 24Claude Code +9 - 0.554score
alhazen-core
sciknow-io
REQUIRED FIRST — starts TypeDB + dashboard via Docker, loads the Alhazen base schema.
SkillCFreshness: C★ 24Claude Code +9 - 0.551score
web-search
sciknow-io
Search the web using SearXNG (self-hosted metasearch - no API key needed)
SkillCFreshness: C★ 24Claude Code +9 - 0.551score
curation-skill-builder
sciknow-io
Design and implement TypeDB-backed curation skills using the Skillful Alhazen methodology
SkillCFreshness: C★ 24Claude Code +9 - 0.551score
typedb-notebook
sciknow-io
Store and retrieve knowledge in the Alhazen TypeDB knowledge graph - remember, recall, organize papers and notes
SkillCFreshness: C★ 24Claude Code +9 - 0.300score
agent-os
sciknow-io
No description from the publisher yet.
SkillCFreshness: C★ 24Claude Code +9
Signals
- Stars
- 24
- Forks
- 3
- Open issues
- 17
- Last commit (this folder)
- Jul 9, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/sciknow-io/skillful-alhazen
- Website
- sciknow.io/alhazen
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from sciknow-io
- 0.554score
alhazen-core
sciknow-io
REQUIRED FIRST — starts TypeDB + dashboard via Docker, loads the Alhazen base schema.
SkillCFreshness: C★ 24Claude Code +9 - 0.554score
agentic-memory
sciknow-io
TypeDB-backed ontological memory with schema-driven retrieval.
SkillCFreshness: C★ 24Claude Code +9 - 0.551score
typedb-notebook
sciknow-io
Store and retrieve knowledge in the Alhazen TypeDB knowledge graph - remember, recall, organize papers and notes
SkillCFreshness: C★ 24Claude Code +9 - 0.551score
web-search
sciknow-io
Search the web using SearXNG (self-hosted metasearch - no API key needed)
SkillCFreshness: C★ 24Claude Code +9 - 0.551score
curation-skill-builder
sciknow-io
Design and implement TypeDB-backed curation skills using the Skillful Alhazen methodology
SkillCFreshness: C★ 24Claude Code +9 - 0.507score
analyst
sciknow-io
Use when researching a question that serves a decision, running market/competitive/internal research, verifying AI-generated research, or aggregating…
SkillCFreshness: C★ 2Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get your-domain-name running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
your-domain-name skill. Agent Mart. https://agentmart.dev/skills/sciknow-io-your-domain-name (last updated 2026-10-24)