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 serejaris/kimi-skills --skill deep-research-swarmgit clone --depth 1 https://github.com/serejaris/kimi-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/serejaris/kimi-skills/deep-research-swarm)<a href="https://agentmods.dev/skills/serejaris/kimi-skills/deep-research-swarm"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/deep-research-swarm/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/serejaris/kimi-skills/deep-research-swarm"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/deep-research-swarm.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 | $0.00000 | $0.06624 |
| Opus 5 | $0.00000 | $0.03312 |
| Sonnet 5 | $0.00000 | $0.01325 |
| Haiku 4.5 | $0.00000 | $0.00662 |
Grade A, and why
deep-research-swarm 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 9d 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 — 510 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research
Orchestrate multi-agent epistemic triangulation: diverge across research dimensions, detect overlaps and contradictions, verify deeply, then converge into a validated synthesis. Swarm parallelism serves epistemic robustness — not merely speed.
Adaptive routing ensures the pipeline fits the task: wide-search topics get a two-stage parallel swarm (breadth then depth); file-based tasks skip or augment external search; focused queries go straight to dimension decomposition.
Output Directory — MANDATORY
All deep research output files MUST be saved under:
/mnt/agents/output/research/
This is non-negotiable. Every file produced in any phase MUST use this directory as the base path. Do NOT save any research artifact to /mnt/agents/output/ directly — always use the /mnt/agents/output/research/ subdirectory.
Before writing any file, ensure the directory exists (create it if not).
Workflow Overview
User Query
│
▼
Phase 0: Intent & Input Router
│
├─ Route A: Wide Search (broad/exploratory, no clear dimensions)
│ → Phase 1 (Quick Landscape)
│ → Phase 1W (Multi-Agent Wide Exploration) ★ NEW
│ → Phase 2 (Decompose, informed by rich landscape)
│ → Phase 3 (Parallel Deep Dive)
│ → Phase 4 (Cross-Verify) → Phase 5 (if conflicts)
│ → Phase 6 (Insight Extraction) → Phase 7 (Report via writing skill)
│
├─ Route B: Focused Search (specific question, clear dimensions)
│ → Phase 1 (Landscape) → Phase 2 (Decompose)
│ → Phase 3 (Parallel Deep Dive) → Phase 4 (Cross-Verify)
│ → Phase 5 (if conflicts) → Phase 6 (Insight Extraction) → Phase 7 (Report)
│
├─ Route C: File-Only Research (user explicitly restricts to file content)
│ → Phase F (File Intake & Deep Analysis) ★ NEW
│ → Phase 2 (Decompose from file themes)
│ → Phase 3-F (Multi-Agent File Deep Dive, NO external search)
│ → Phase 4 (Cross-Verify across file analyses)
│ → Phase 6 (Insight Extraction) → Phase 7 (Report via writing skill)
│
└─ Route D: File-Augmented Research (files as primary reference + external supplement)
→ Phase F (File Intake & Deep Analysis) ★ NEW
→ Phase 1 (Targeted Landscape, informed by file gaps)
→ Phase 2 (Decompose, merging file themes + external landscape)
→ Phase 3 (Parallel Deep Dive, each agent has file context + search)
→ Phase 4 (Cross-Verify) → Phase 5 (if conflicts)
→ Phase 6 (Insight Extraction) → Phase 7 (Report via writing skill)
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.
- 9d ago First seen · 510 lines · 0 tokens per session scan A a705d79e6e26
deep-research-swarm is a skill published in the GitHub repository serejaris/kimi-skills (6 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 6,624 tokens. 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 2026-09-03.
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