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 skills add agentlas-ai/agentlas-desktop --skill researchgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/research)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/research"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/research"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/research.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00032 | $0.00363 |
| Opus 5.5 | $0.00013 | $0.00145 |
| Sonnet 5.5 | $0.00006 | $0.00073 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
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 1mo 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
Skill Purpose
Discovers and synthesizes real user problems, onboarding hurdles, and interface friction from public communities and internal feedback sources into a prioritized, actionable product brief.
Preconditions
- The target product name or service identifier must be provided.
- Strictly adhere to
$critical-overrides.
Steps
- Define Scope: Specify target product surface, user personas, and focus areas (e.g., onboarding, checkout, API integration, settings).
- Search Public & Internal Sources:
- Query public forums (Reddit, X, Hacker News, Stack Overflow, GitHub issues/discussions, user reviews).
- Query internal resources (issue trackers, CRM notes, support tickets) when accessible.
- Cluster & Categorize Findings:
- Product UI/workflow friction
- Onboarding and setup obstacles
- Billing, account, and permissions friction
- Documentation and developer experience gaps
- Reliability and performance concerns
- Prioritize Findings:
- Score and rank issues by severity, frequency, evidence confidence, and product leverage.
- Categorize recommendations into immediate fixes (this week), near-term improvements (this quarter), and areas for deeper discovery.
- Generate Research Brief: Deliver an executive summary, ranked issue list, and source citation map.
Outputs
- Prioritized UX research brief with source citations.
- Product opportunity roadmap and recommendations.
Verification
- Ensure every documented problem is backed by explicit citations or recorded user evidence.
- Verify that subjective opinions are distinguished from recurring, high-frequency user friction.
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.
- 1mo ago First seen · 36 lines · 32 tokens per session scan A ac186b80dc02
research is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 32 tokens to every session and 363 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-06.
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