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 valtterimelkko/agent-workflow-skills --skill deep-researchgit clone --depth 1 https://github.com/valtterimelkko/agent-workflow-skillsWrote 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/valtterimelkko/agent-workflow-skills/deep-research)<a href="https://agentmods.dev/skills/valtterimelkko/agent-workflow-skills/deep-research"><img src="https://agentmods.dev/badge/skills/valtterimelkko/agent-workflow-skills/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/valtterimelkko/agent-workflow-skills/deep-research"><img src="https://agentmods.dev/badge/skills/valtterimelkko/agent-workflow-skills/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 | $0.00104 | $0.04040 |
| Opus 5 | $0.00052 | $0.02020 |
| Sonnet 5 | $0.00021 | $0.00808 |
| Haiku 4.5 | $0.00010 | $0.00404 |
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 10d 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 — 490 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research Skill
A comprehensive workflow for conducting broad, source-rich, auditable research using iterative waves of subagents.
Time: ~6–10 minutes
Sources: 40–100+
Best for: academic rigor, due diligence, comparative analysis, high-stakes synthesis
The strength of this skill is breadth with control: it covers many dimensions without letting any single subagent become the whole research system.
Core Operating Model
The main agent is the research conductor.
Subagents are used for:
- scouting source landscapes
- analysing curated source batches
- resolving contradictions
- checking synthesis quality
The main agent is responsible for:
- defining dimensions
- maintaining the research manifest
- maintaining the blocked-source registry
- curating which sources get promoted to deeper analysis
- deciding whether the research is sufficient
- consulting the user on fallback options when high-value blocked sources remain
Core Principles
- Breadth is preserved at the workflow level, not overloaded into each subagent.
- Subagents must return useful findings in their response text first. File writes are secondary.
- Subagents must not silently switch to non-default fallback workflows such as browser automation. If blocked, they must report and move on.
- Forward progress is preferred unless a critical gap is identified.
- Blocked-source review happens at the end of the standard workflow, before considering fallback workflows.
- Fallback workflows require explicit user consultation.
Research Tier Positioning
Use this skill as the heaviest general-purpose research tier:
- Native
web_search/web_fetch— one-off current facts, a single page, or a few URLs gpt-research— quick / medium-depth synthesis across roughly 10-20 sourcesdeep-research(this skill) — 40+ sources, explicit methodology, high-stakes synthesis- Specialised recency skills such as
last30days— when the main need is recent discourse across social/web platforms rather than broad general-web synthesis
What ships with it
7 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.
- 10d ago First seen · 490 lines · 104 tokens per session scan A edb6ac2a962b
deep-research is a skill published in the GitHub repository valtterimelkko/agent-workflow-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 104 tokens to every session and 4,040 once invoked, about $0.0005 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 2026-08-31.
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