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/proxy2021/enso/enso-researchernpx skills add Proxy2021/Enso --skill enso-researchergit clone --depth 1 https://github.com/Proxy2021/EnsoWrote 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/proxy2021/enso/enso-researcher)<a href="https://agentmods.dev/skills/proxy2021/enso/enso-researcher"><img src="https://agentmods.dev/badge/skills/proxy2021/enso/enso-researcher.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 | $0.00748 |
| Opus 5 | $0.00011 | $0.00374 |
| Sonnet 5 | $0.00004 | $0.00150 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
enso-researcher 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.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enso Researcher
Deep web research with multi-query coverage, AI-synthesized narrative, images, videos, and a persistent research library.
When to use
Use the researcher tools when:
- The user asks to research, investigate, or look into a topic
- A question needs multiple sources for a thorough answer (not a quick factual lookup)
- The user wants a comprehensive overview with citations
- Comparing two approaches, technologies, or perspectives
- Following up on a previously researched topic with a specific question
Do NOT use for:
- Simple factual questions ("What's the capital of France?")
- Tasks that need real-time data (stock prices, live scores)
- Requests the agent can answer from its own knowledge
Tools
enso_researcher_search (start here)
Primary entry point. Runs multiple web queries, fetches sources, and synthesizes findings with Gemini.
enso_researcher_search({ topic: "quantum computing applications", depth: "standard" })
depth:quick(3 queries, fast),standard(6 queries, balanced),deep(8 queries, thorough)- Results are cached — repeated searches return instantly from the research library
- Use
force: trueto bypass cache and get fresh results - Returns: narrative, key findings, sections, sources, images, videos
enso_researcher_deep_dive
Drill into a specific subtopic after an initial search.
enso_researcher_deep_dive({ topic: "quantum computing", subtopic: "error correction breakthroughs" })
enso_researcher_compare
Side-by-side comparison of two topics with structured analysis.
enso_researcher_compare({ topicA: "React", topicB: "Vue", context: "for a startup MVP" })
enso_researcher_follow_up
Ask a specific follow-up question in the context of prior research.
enso_researcher_follow_up({ topic: "quantum computing", question: "What are the main challenges for commercial adoption?" })
enso_researcher_send_report
Email a full research report to a recipient. Pulls all data (narrative, findings, sections, sources, media) from the research cache automatically — just provide recipient and topic.
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 · 94 lines · 22 tokens per session scan A 97f37b4211bd
enso-researcher is a skill published in the GitHub repository Proxy2021/Enso (5 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 748 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
speckit-specify
Create or update the feature specification from a natural language feature description.
speckit-analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
add-sample
Create a SamplesApp sample page with correct theming and attributes. Use when adding UI samples for controls.
speckit-taskstoissues
Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.
speckit-git-remote
Detect Git remote URL for GitHub integration.
review-agents-md
Audit Dograh AGENTS.md files for drift against the live repo and for bad scope boundaries between parent and child docs. Use when the user asks to review existing AGENTS files, identify stale guidance, decide whether a subtree needs its own AGENTS.md, or update the AGENTS.md hierarchy under the repo root, api/, or ui/.