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 bonnguyenitc/specship --skill ss-researchgit clone --depth 1 https://github.com/bonnguyenitc/specshipWrote 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/bonnguyenitc/specship/ss-research)<a href="https://agentmods.dev/skills/bonnguyenitc/specship/ss-research"><img src="https://agentmods.dev/badge/skills/bonnguyenitc/specship/ss-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/bonnguyenitc/specship/ss-research"><img src="https://agentmods.dev/badge/skills/bonnguyenitc/specship/ss-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 | $0.00122 | $0.01833 |
| Opus 5.5 | $0.00049 | $0.00733 |
| Sonnet 5.5 | $0.00024 | $0.00367 |
| Haiku 4.5 | $0.00012 | $0.00183 |
Grade A, and why
ss-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.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research
Goal: turn a question into a current, verified, cited answer. Model memory has a training cutoff and no sources — never answer a time-sensitive or external-fact question from memory alone.
When to use
- "Research X", "compare X vs Y", "what's the best library for X?".
- Choosing a dependency, API, service, or architecture that external facts decide.
- Anything where "latest", "current", version numbers, pricing, or dates matter.
- A pipeline stage (
spec,plan) hits an open question only the outside world can answer.
Not for questions the repo itself answers — use ss-explore-source / code search
for those.
Step 1 — Frame the question
Before any search, pin down (ask the user only if the answer changes the work):
- Decision: what will this research be used for? Research feeding "pick a library" needs comparison criteria; "how does this API work" needs docs.
- Freshness: does it need this week's state (releases, pricing) or stable knowledge (algorithms, standards)?
- Depth: quick answer, comparison table, or deep report? Match effort to it.
Step 2 — Pick the strongest search tool available
Inventory the session's tools first — list what's actually connected (MCP servers, built-ins) instead of defaulting to the generic web search. Then pick the highest rung available on this ladder:
- Specialized search MCP — e.g. Exa, Perplexity, Tavily, Brave/Kagi Search, Firecrawl. These return richer, fresher, less SEO-polluted results than generic search; if one is connected, it is the default for open-web questions.
- Domain-specific MCP when the question has a domain — library/API docs: Context7, DeepWiki; repos/issues/PRs: a GitHub MCP; internal knowledge: Notion/Confluence/Slack MCPs. A docs tool beats web search for "how do I use library X" every time.
- Built-in web search + URL fetch — the fallback when no MCP search is connected. Still fine; just expect more noise.
- Browser automation — last resort, only for pages that need JS rendering or a login the fetch tools can't handle.
What ships with it
1 file 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.
- 1mo ago First seen · 161 lines · 122 tokens per session scan A 77719c11f61b
ss-research is a skill published in the GitHub repository bonnguyenitc/specship (2 stars, last pushed 2mo ago), licensed MIT. It adds 122 tokens to every session and 1,833 once invoked, about $0.0005 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 2026-08-31.
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