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 comisai/comis --skill deep-researchgit clone --depth 1 https://github.com/comisai/comisWrote 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/comisai/comis/deep-research)<a href="https://agentmods.dev/skills/comisai/comis/deep-research"><img src="https://agentmods.dev/badge/skills/comisai/comis/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/comisai/comis/deep-research"><img src="https://agentmods.dev/badge/skills/comisai/comis/deep-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 | $0.00117 | $0.01795 |
| Opus 5.5 | $0.00047 | $0.00718 |
| Sonnet 5.5 | $0.00023 | $0.00359 |
| Haiku 4.5 | $0.00012 | $0.00179 |
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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Systematic methodology for thorough web research. Load this skill BEFORE starting any content generation task to gather sufficient information from multiple angles, depths, and sources.
Core Principle
Never generate content based solely on general knowledge. The quality of output depends directly on research quality. A single search query is never enough.
For context-dependent follow-ups about attribution, failed sources, or compression, re-fetch every candidate citation from the existing research before using it in the new answer and preserve the user's requested format. If the user says a source is down but no failed receipt identifies one, do not invent an unavailable URL; report only failures observed during re-fetch.
Before answering, obtain at least three distinct successful web_search query receipts covering different research angles and at least three distinct successful web_fetch receipts from three different URLs. If fewer than three successful query receipts or source receipts can be obtained, label the result partial or incomplete, name each unavailable source or capability blocker, and do not fill the evidence gap from memory.
Build a receipt ledger keyed by canonical URL before writing. Re-fetching the same URL does not count as another source, even when the options or returned length differ. Continue fetching until the ledger has three unique successful URLs; otherwise abstain from a substantive answer and return only the incomplete-research status.
Every factual paragraph or claim cluster in the answer must carry an inline citation to one or more fetched URLs that support it. Omit any statement the fetched sources do not support. On every failed source fetch, name the failed URL in an Unavailable sources note with its error; never cite or use that source as evidence.
Every URL presented as a citation must have a successful web_fetch receipt from the current research run. A web_search result or snippet is discovery evidence, not citation evidence. Fetch a discovered source before citing it; if the fetch fails, omit it from citations and name it separately as an attempted but unavailable source.
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 · 159 lines · 117 tokens per session scan A f688eccbedb5
deep-research is a skill published in the GitHub repository comisai/comis (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 117 tokens to every session and 1,795 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.
Other skills, from other repositories
takt
A workflow engine that runs a task through a sequence of steps defined in a YAML file, using separate coding agents for the work.
pandic-office
Convert Markdown to PDF (or DOCX/EPUB/HTML) using the pandoc CLI. Use when asked to produce a PDF report, brief, summary, or any document where the input is Markdown and the output should be a polished, paginated file.
tmux
Manage long-running shell sessions with tmux — start a detached session, run a long task, reattach later, capture output. Use when a task takes longer than a single tool call (build, test, log tail).
skill-creator
Generates Anthropic Skills with complete workflow including GitHub PR creation and local download verification.
codebase-context-extractor
This skill provides a comprehensive context extraction system for large codebases. It intelligently analyzes code structure, dependencies, and relationships to extract relevant context for understanding, debugging, or modifying code.
dd-synthesis
Synthesize findings from multiple specialist agents into a coherent investment recommendation. Use when combining reports from 9+ specialist agents into a single decision document.