Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/arch3rpro/ark-space/web-researchnpx skills add arch3rPro/ark-space --skill web-researchgit clone --depth 1 https://github.com/arch3rPro/ark-spaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/arch3rpro/ark-space/web-research)<a href="https://agentmods.dev/skills/arch3rpro/ark-space/web-research"><img src="https://agentmods.dev/badge/skills/arch3rpro/ark-space/web-research.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00022 | $0.00188 |
| Opus 5 | $0.00011 | $0.00094 |
| Sonnet 5 | $0.00004 | $0.00038 |
| Haiku 4.5 | $0.00002 | $0.00019 |
Grade A, and why
web-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Web Research
Synthesize cited research across multiple public sources through the configured provider selected by registry/deep-research-providers.yaml. For ambiguous web intent, apply workflows/web-capability-routing.md. Accept a research prompt plus portable source, domain, timeout, and output controls. Report the actual provider, citations, request ID when supplied, and any fallback.
- Prefer Tavily for broad or long-form research synthesis.
- Prefer Exa for concise, focused cited answers.
- Do not silently substitute a user-requested provider.
Run
python3 <installed-arkspace-path>/scripts/arkspace.py research run --provider tavily "AI coding agents market" --output json
For setup or a failed provider check, use provider-manager.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 21 lines · 22 tokens per session scan A 75fd729e9720
web-research is a skill published in the GitHub repository arch3rPro/ark-space (2 stars, last pushed 9d ago), licensed MIT. It adds 22 tokens to every session and 188 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
invest
First-time fork users: run the invest-setup skill first to initialize. The main flow lives in this skill (the AI agent uses the CLI/MCP to view the portfolio / run the committee / replay decision history). The Web GUI has been retired (2026-07) — every capability is exposed via CLI subcommands / MCP tools. The backend…
invest-setup
First-time openInvest installation and onboarding. ONLY use when user explicitly says "set up invest" / "init invest" / "帮我初始化 invest", OR when invest skill's doctor returns status="needssetup". NOT for daily usage — once onboarding is done, the invest skill takes over (portfolio viewing, committee analysis, buy/sell…
okf-frontmatter
Maintain openInvest's docs (docs/wiki chapters + docs/wiki/adr) under Google's Open Knowledge Format (OKF). Two jobs. (1) Teach agents to maintain docs the OKF way — every doc carries a small YAML frontmatter block as the single source of truth (type, title, tags, intent, schemasource, documents); schema details link…
invest-backup
Back up / restore openInvest's local state — memory/ (holdings, strategy, user profile, committee records, dream logs) + db/ (trade ledger, job run history, market-data cache) + .env (SMTP/API credentials) + userprofile.json. All of this data is .gitignore'd with no historical versions in git, so a single accidental…
openehr-assistant
This skill should be used when a conversation touches openEHR outside a task owned by a dedicated skill — e.g. "what is an archetype?", "how do openEHR templates work?", "which composition category fits this data?", "find me a guide on X", "where do I start with openEHR modeling?" — or names openEHR concepts (ADL…
template-authoring
This skill should be used when the user asks to "create a template", "design a template", "constrain archetypes into a template", "review a template", "categorise a dataset with CGEM", "should this be persistent, episodic or event?", "split this form across compositions", "sketch a template from this form / which…