Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/jimezsa/opencolab/deep-research)<a href="https://agentmods.dev/skills/jimezsa/opencolab/deep-research"><img src="https://agentmods.dev/badge/skills/jimezsa/opencolab/deep-research.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 5.1 | $0.00032 | $0.03806 |
| Opus 5 | $0.00016 | $0.01903 |
| Sonnet 5 | $0.00006 | $0.00761 |
| Haiku 4.5 | $0.00003 | $0.00381 |
Grade B, and why
deep-research scanned grade B with 2 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 6d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
"script": "git clone https://github.com/jimezsa/papercli.git && cd papercli && make build && sudo install -m 0755 ./bin/papercli /usr/local/bin/papercli", Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://raw.githubusercontent.com/jimezsa/papercli/main/SKILLS/deep-research/SKILL.md \ How it starts
The opening of the file, as written. The whole thing — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research Skill
Use this skill for comprehensive scientific research tasks such as state-of-the-art reviews, deep comparisons, research strategy, and evidence-heavy decision support.
If the user later asks an exact follow-up question about a downloaded paper or wants a bounded local verification pass, switch to pageindex-grounded for grounded retrieval over the existing PDF corpus.
Update This Skill
Only do this if the user explicitly asks to update this skill from the GitHub repo.
To refresh this skill directly from the GitHub repo:
curl -fsSL https://raw.githubusercontent.com/jimezsa/papercli/main/SKILLS/deep-research/SKILL.md \
-o SKILLS/deep-research/SKILL.md
Mission
Deliver an institutional-grade findings.md by:
- Running iterative
papercliretrieval across multiple query waves. - Downloading and reading a broad, diverse paper corpus.
- Extracting core ideas, concepts, results, assumptions, and key mathematics.
- Producing a detailed markdown report inside a topic-scoped research run folder, where all claims are grounded by references.
- Producing a companion literature-map block diagram that shows how the main papers or paper families connect.
Prerequisites
papercliis installed and available inPATH.
Non-Negotiable Rules
- Use
paperclias the retrieval backbone. - Read paper content from downloaded PDFs whenever possible.
- Never present uncited factual claims.
- Surface conflicts and uncertainty explicitly.
- Final output must be a detailed markdown file named
findings.mdinside the active research run folder. - Each distinct topic must live in its own dated, topic-slugged folder under
research/. - Maintain
research/INDEX.mdand the run-localRUN.mdmetadata file so later agents can recognize what each research folder contains. - After synthesis, produce a companion literature-map diagram through the shared
block-diagramskill. - The literature map must only show evidence-backed relations such as method lineage, direct comparison, shared benchmark or dataset, critique, or common problem framing.
- Do not invent paper-to-paper influence or citation edges that are not supported by the corpus.
- OpenColab normally provides
OPENCOLAB_PROGRESS_FILEduring provider runs. When it is set, emit bounded JSON progress updates for long-running stages instead of remaining silent until the end.
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.
- 6d ago First seen · 422 lines · 32 tokens per session scan B d8cef395a933
deep-research is a skill published in the GitHub repository jimezsa/opencolab (11 stars, last pushed 23d ago), licensed MIT. It adds 32 tokens to every session and 3,806 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 2 findings (asks for root, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
bias-detection
Assess systematic biases in the evidence body — publication bias, reporting bias, and selective outcome reporting. Budget: 40 studies, 40 effect sizes, 40 web searches.
assumption-audit
Surface all assumptions, classify by vulnerability (load-bearing × likely-false), validate causal logic. Focus on dangerous assumptions — high load-bearing + non-explicit.
anomaly-characterization
SOP: Describe and classify anomalous phenomena that existing theory cannot explain.
boundary-condition-specification
SOP: Specify the boundary conditions under which a hypothesis holds.
causal-tree-building
Build logical causal trees from symptoms to root causes — list UDEs, connect causal chains, validate logic, locate root causes. Combines ishikawa-decomposition, current-reality-tree, and clr-validation SOPs.
competing-hypothesis-generation
SOP: Generate mechanistically distinct competing hypotheses for the same phenomenon.