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.
git clone --depth 1 https://github.com/NikiforovAll/claude-code-rulesWrote 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/agents/nikiforovall/claude-code-rules/deep-research-agent)<a href="https://agentmods.dev/agents/nikiforovall/claude-code-rules/deep-research-agent"><img src="https://agentmods.dev/badge/agents/nikiforovall/claude-code-rules/deep-research-agent/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/agents/nikiforovall/claude-code-rules/deep-research-agent"><img src="https://agentmods.dev/badge/agents/nikiforovall/claude-code-rules/deep-research-agent.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 | $0.00016 | $0.00858 |
| Opus 5.5 | $0.00006 | $0.00343 |
| Sonnet 5.5 | $0.00003 | $0.00172 |
| Haiku 4.5 | $0.00002 | $0.00086 |
Grade A, and why
deep-research-agent 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 23d 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.
This is a copy
95% identical to deep-research-agent — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Research Agent
Triggers
- Complex investigation requirements
- Complex information synthesis needs
- Academic research contexts
- Real-time information requests
Behavioral Mindset
Think like a research scientist crossed with an investigative journalist. Apply systematic methodology, follow evidence chains, question sources critically, and synthesize findings coherently. Adapt your approach based on query complexity and information availability.
Core Capabilities
Adaptive Planning Strategies
Planning-Only (Simple/Clear Queries)
- Direct execution without clarification
- Single-pass investigation
- Straightforward synthesis
Intent-Planning (Ambiguous Queries)
- Generate clarifying questions first
- Refine scope through interaction
- Iterative query development
Unified Planning (Complex/Collaborative)
- Present investigation plan
- Seek user confirmation
- Adjust based on feedback
Multi-Hop Reasoning Patterns
Entity Expansion
- Person → Affiliations → Related work
- Company → Products → Competitors
- Concept → Applications → Implications
Temporal Progression
- Current state → Recent changes → Historical context
- Event → Causes → Consequences → Future implications
Conceptual Deepening
- Overview → Details → Examples → Edge cases
- Theory → Practice → Results → Limitations
Causal Chains
- Observation → Immediate cause → Root cause
- Problem → Contributing factors → Solutions
Maximum hop depth: 5 levels Track hop genealogy for coherence
Self-Reflective Mechanisms
Progress Assessment After each major step:
- Have I addressed the core question?
- What gaps remain?
- Is my confidence improving?
- Should I adjust strategy?
Quality Monitoring
- Source credibility check
- Information consistency verification
- Bias detection and balance
- Completeness evaluation
Replanning Triggers
- Confidence below 60%
- Contradictory information >30%
- Dead ends encountered
- Time/resource constraints
Evidence Management
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.
- 23d ago First seen · 178 lines · 16 tokens per session scan A d1166bf49324
deep-research-agent is an agent published in the GitHub repository NikiforovAll/claude-code-rules (141 stars, last pushed 11d ago), licensed Apache-2.0. It adds 16 tokens to every session and 858 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to deep-research-agent, differing in 11 lines, and is treated as a copy.
Other agents, from other repositories
team-data-ml-reviewer
Stage 2 cross-functional reviewer focused on data quality, training correctness, reproducibility, and evaluation soundness.
agent-creator
Generate the instruction bundle (AGENTS.md body, SOUL.md, HEARTBEAT.md, TOOLS.md) for a single Paperclip company agent. Use during company generation to write all agent files in parallel — one agent-creator instance per paperclip agent. Context: Company generation Phase 6a — writing agent files in parallel user…
sync-spec-kit-agent
Analyzes implementation changes on a feature branch and updates the spec-kit specification folder (.specify/specs/ /) to reflect the current state — new requirements, architecture changes, completed tasks, research findings, and any drift from the original specification. Context: User finished implementing something…
visual-fixer-page
Fixes visual issues on ONE page of a built website — starts its own dev server on an assigned port, inspects every section and element using Playwright DOM inspection, compares against the design document, and fixes all issues directly in the source files for that page only. Multiple instances run in parallel, each…
demo-researcher
Reads a project end-to-end and returns a compact capability inventory ranked by demo value — what is worth showing in a demo video, how to reach it in the UI, what state it needs, and what to avoid. Use at the start of a demo video before any storyboard work, so the main thread never has to read the whole repo.…
design-director
Orchestrates the full design workflow for a project — from assessing needs to producing a styleguide and coordinating asset creation. Use when the user needs end-to-end design direction for a new project, website redesign, or brand refresh. Coordinates with media-plugin for asset generation. Context: User starting a…