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 robotlearning123/gpt2agent --skill deep-researchgit clone --depth 1 https://github.com/robotlearning123/gpt2agentWrote 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/robotlearning123/gpt2agent/deep-research)<a href="https://agentmods.dev/skills/robotlearning123/gpt2agent/deep-research"><img src="https://agentmods.dev/badge/skills/robotlearning123/gpt2agent/deep-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/robotlearning123/gpt2agent/deep-research"><img src="https://agentmods.dev/badge/skills/robotlearning123/gpt2agent/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00161 | $0.02230 |
| Opus 5.5 | $0.00064 | $0.00892 |
| Sonnet 5.5 | $0.00032 | $0.00446 |
| Haiku 4.5 | $0.00016 | $0.00223 |
Grade A, and why
deep-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/deep-research — ChatGPT Pro Deep Research
Calls gpt2agent's deep_research / deep_research_heavy directly via
pipx Python (bypasses MCP — works even before Claude Code session restart).
Preconditions (check once)
command -v gpt2agent >/dev/null || \
echo "gpt2agent not installed; run: pipx install gpt2agent"
test -f "${CODEX_HOME:-$HOME/.codex}/auth.json" || test -f "$HOME/.gpt2agent/token.json" || \
echo "ChatGPT token missing; run: codex login or gpt2agent setup"
~/.claude/skills/deep-research/bin/quota.sh # prints remaining DR quota
Usage
~/.claude/skills/deep-research/bin/run.sh [--heavy] [-o OUT_DIR] "<query>"
- Default mode: light (
deep_research, ~1 min, citations included). --heavy: deep_research_heavy (5-30 min, gpt-6-pro + connector). The connector renders an embedded-UI widget; the report is recovered from the hidden widget state (widget_state.report_message) via?include_visually_hidden_messages=true&include_widget_state=true— see "Heavy DR retrieval" below.-o OUT_DIR: output directory (default: a unique./research/dr-YYYYMMDD-HHMMSS-*/directory).- Query can be inline string,
-for stdin, or@file.mdto read from file.
The script writes:
report.md— final report (reconstructed for heavy mode)events.jsonl— all raw SSE events (for debugging / re-extraction)status.txt— START / DONE / INCOMPLETE / ERROR with elapsed seconds + event countsmeta.json— server metadata (model slug, request id, etc.)
The run directory is restricted to mode 0700 and its artifacts to 0600 on
POSIX systems because queries, reports, and metadata may be sensitive.
When to invoke
| Situation | Mode |
|---|---|
| Quick factual question with citations | light |
| Literature review, market scan, technical decision matrix | light |
| Big strategic question (>5 questions, want 10+ KB report) | heavy |
| Question that might yield 50+ sources | heavy |
Skip this skill for: pure code questions, debugging, tasks the user
explicitly wants you to handle locally, anything covered by context7
(library docs) or local files.
What ships with it
3 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.
- 1mo ago Changed · +2 lines 6cbd3b134a8d
- 1mo ago Changed · +9 lines 38ed12b770d6
- 1mo ago Changed · -2 tokens per session ad4cf845ba8a
- 1mo ago First seen · 170 lines · 163 tokens per session scan A 39ebc3c77c1c
deep-research is a skill published in the GitHub repository robotlearning123/gpt2agent (52 stars, last pushed 5d ago), licensed MIT. It adds 161 tokens to every session and 2,230 once invoked, about $0.0006 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 2026-08-30.
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