DeerFlow is an open-source super-agent harness that coordinates sub-agents, memory, tools, sandboxes, and extensible skills to handle research, coding, and content-creation tasks that may run for minutes or hours. It is intended for long-running, multi-step work performed by AI agents. The catalogue entries are skills, agents, and instructions that support its workflows.
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 bytedance/deer-flow --skill consulting-analysisgit clone --depth 1 https://github.com/bytedance/deer-flowWrote 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/bytedance/deer-flow/consulting-analysis)<a href="https://agentmods.dev/skills/bytedance/deer-flow/consulting-analysis"><img src="https://agentmods.dev/badge/skills/bytedance/deer-flow/consulting-analysis/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/bytedance/deer-flow/consulting-analysis"><img src="https://agentmods.dev/badge/skills/bytedance/deer-flow/consulting-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
- Socket pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low Excessive Agency · line 3 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00110 | $0.07257 |
| Opus 5.5 | $0.00044 | $0.02903 |
| Sonnet 5.5 | $0.00022 | $0.01451 |
| Haiku 4.5 | $0.00011 | $0.00726 |
Grade A, and why
consulting-analysis 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- consulting-analysis — 100% identical, 0 lines differ
- consulting-analysis — 100% identical, 0 lines differ
- consulting-analysis — 100% identical, 0 lines differ
- consulting-analysis — 100% identical, 0 lines differ
- consulting-analysis — 100% identical, 0 lines differ
- consulting-analysis — 100% identical, 0 lines differ
- consulting-analysis — 98% identical, 12 lines differ
- consulting-analysis — 98% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 632 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Professional Research Report Skill
Overview
This skill produces professional, consulting-grade research reports in Markdown format, covering domains such as market analysis, consumer insights, brand strategy, financial analysis, industry research, competitive intelligence, investment research, and macroeconomic analysis. It operates across two distinct phases:
- Phase 1 — Analysis Framework Generation: Given a research subject, produce a rigorous analysis framework including chapter skeleton, per-chapter data requirements, analysis logic, and visualization plan.
- Phase 2 — Report Generation: After data has been collected by other skills, synthesize all inputs into a final polished report.
The output adheres to McKinsey/BCG consulting voice standards. The report language follows the output_locale setting (default: zh_CN for Chinese).
Data Authenticity Protocol
Strict Adherence Rule: All data presented in the report and visualized in charts MUST be derived directly from the provided Data Summary or External Search Findings.
- NO Hallucinations: Do not invent, estimate, or simulate data. If data is missing, state "Data not available" rather than fabricating numbers.
- Traceable Sources: Every major claim and chart must be traceable back to the input data package.
Core Capabilities
- Design analysis frameworks from scratch given only a research subject and scope
- Transform raw data into structured, high-depth research reports
- Follow the "Visual Anchor → Data Contrast → Integrated Analysis" flow per sub-chapter
- Produce insights following the "Data → User Psychology → Strategy Implication" chain
- Embed pre-generated charts and construct comparison tables
- Generate inline citations formatted per GB/T 7714-2015 standards
- Output reports in the language specified by
output_localewith professional consulting tone - Adapt analytical depth and structure to domain (marketing, finance, industry, etc.)
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 · 632 lines · 110 tokens per session scan A 5bc99df7e5f8
consulting-analysis is a skill published in the GitHub repository bytedance/deer-flow (83,916 stars, last pushed today), licensed MIT. It adds 110 tokens to every session and 7,257 once invoked, about $0.0004 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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