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 ssmurfgg04-gif/context-m --skill research-explorergit clone --depth 1 https://github.com/ssmurfgg04-gif/context-mWrote 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/ssmurfgg04-gif/context-m/research-explorer)<a href="https://agentmods.dev/skills/ssmurfgg04-gif/context-m/research-explorer"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/research-explorer/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/ssmurfgg04-gif/context-m/research-explorer"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/research-explorer.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 | $0.00056 | $0.01444 |
| Opus 5 | $0.00028 | $0.00722 |
| Sonnet 5 | $0.00011 | $0.00289 |
| Haiku 4.5 | $0.00006 | $0.00144 |
Grade A, and why
research-explorer 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 9d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Explorer
Overview
Research-topic exploration SKILL. Takes a broad direction, performs multi-dimensional web research with the agent's own WebSearch / WebFetch tools, and produces three structured Markdown deliverables. Single stage, full quality from the start. No Python runtime, no LLM SDK.
When to Use
- User says "I want to research X" without a specific topic.
- User wants to know "what are the hot topics in X".
- User needs help narrowing a broad field into 5–10 candidate topics.
- User asks for "research landscape overview".
When NOT to Use
- User already has a specific research question → use
literature-surveyorpaper-writer. - User wants a quick fact-check → use WebSearch directly.
Workflow
Step 1 — Understand the direction
Confirm with the user:
- Direction — the broad area of interest (e.g., "federated learning", "NLP for healthcare").
- Constraints — theory vs. applied, specific methods, target venue, compute budget, time horizon.
- Language — default English in conversation; reports in English unless the user requests otherwise.
Step 2 — Set up the run directory
DIRECTION="<direction>"
SLUG=$(python3 -c "import re,hashlib,sys; t=sys.argv[1]; n=re.sub(r'[\\s_]+','-',re.sub(r'[^\\w\\s-]','',t.lower().strip())).strip('-')[:40].rstrip('-'); h=hashlib.sha1(t.encode()).hexdigest()[:8]; print(f'{n}-{h}')" "$DIRECTION")
TS=$(date +%Y-%m-%d_%H%M%S)
RUN=output/research-explorer/$SLUG/$TS
mkdir -p "$RUN"
ln -sfn "$TS" "output/research-explorer/$SLUG/latest"
In commands below $RUN = output/research-explorer/<slug>/latest.
Step 3 — Multi-dimensional exploration
Run AMiner academic search and web search across the following dimensions (one query per dimension, more if returns are thin):
- Hot topics — " 2024 2025 hot topics" / "recent advances".
- Open problems — " open problems" / "challenges".
- Surveys — " survey 2024" / " review".
- Benchmarks — " benchmark" / " evaluation dataset".
- Applications — " applications" / " industry use cases".
- Cross-field — " + " (pick 1–2 adjacent fields).
- Recent breakthroughs — papers from the last 6–12 months at top venues.
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.
- 9d ago First seen · 137 lines · 56 tokens per session scan A f866d6a5721a
research-explorer is a skill published in the GitHub repository ssmurfgg04-gif/context-m (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 56 tokens to every session and 1,444 once invoked, about $0.0003 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 2026-09-03.
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