Capabilities
Complete guide to Tachikoma's features and capabilities.
Core Capabilities
Intent Classification & Routing
Automatic intent classification routes requests to optimal execution strategies based on task complexity.
- Low Complexity — Direct response (1-2s)
- Medium Complexity — Single skill (5-15s)
- High Complexity — Skill chain (15-45s)
- Very High Complexity — RLM orchestration (45-120s)
Context Management
Position-aware context loading optimizes token placement for maximum effectiveness.
- U-shaped attention bias awareness
- Critical info at start/end
- Context compression at 70-80% utilization
- Structured summaries
Skill Execution
Specialized skills handle specific task types with optimal tool usage.
- 5 core skills (down from 11)
- Dynamic skill loading
- Model-aware operations
- Verification loops for critical tasks
Skill Chains
Orchestrate multiple skills for complex workflows.
- Sequential execution
- State passing between skills
- Error handling
- Verification integration
PAUL Methodology
PAUL (Plan-Apply-Unify Loop) — Structured development framework.
- PLAN — Define objectives and acceptance criteria
- APPLY — Execute with verification
- UNIFY — Close loop
Never skip UNIFY — this is the heartbeat that prevents drift.
Advanced Capabilities
Model-Aware Editing
Dynamic edit format selection optimized for specific LLM models.
str_replace— Claude, Mistral (exact string matching)str_replace_fuzzy— Gemini (fuzzy whitespace)apply_patch— GPT (diff format)hashline— Grok, GLM (content-hash anchoring)
Subagents
Workers for large-context discovery and parallel tasks.
- Codebase exploration
- Parallel search execution
- Long-running sessions
Verification Loops
Generator-Verifier-Reviser pattern for high-stakes implementations.
- GENERATE — Initial solution
- VERIFY — Explicit criteria checking
- REVISE — Fix based on feedback
- Up to 3 iterations
Use for: complex implementations, high-stakes fixes, correctness-critical tasks. Skip for: simple tasks (<50 lines), prototypes, well-understood patterns.
Research-Backed Features
Meta Orchestration
Self-programming agent generation engine for AI-centered agent development.
- Self-generating agent topology — Create specialized subagents on-demand
- Dynamic tool synthesis — Write custom tools at runtime
- Hierarchical memory management — Graph-based knowledge persistence
Cost-Aware Routing
Match task complexity to execution strategy for optimal speed vs accuracy.
Research: "When Do Tools and Planning Help LLMs Think?" (arXiv:2601.02663)
Position-Aware Context
Optimize for tokens-per-task using U-shaped attention patterns.
Research: "Found in the Middle" (ACL 2024)
Model Harness
Edit format selection matters as much as model choice.
Source: Can.ac blog (Feb 2026)
Capability Matrix
| Capability | Best For | Complexity | Latency |
|---|---|---|---|
| Direct Response | Simple queries, <50 lines | Low | 1-2s |
| Single Skill | Focused tasks, one domain | Medium | 5-15s |
| Skill Chain | Multi-step workflows | High | 15-45s |
| Verification Loop | High-stakes, correctness-critical | High | +10-30s |
| Subagent | Large-context discovery, parallel tasks | Very High | 45-120s |
| RLM | Massive contexts, 10M+ tokens | Very High | 2-5min |
Decision Flow
User Request
↓
Classify Intent
↓
Confidence > 0.7?
├── NO → Ask for clarification
↓ YES
Context > 2000 tokens?
├── YES → Use RLM subagent
↓ NO
Task Complexity?
├── Simple → Direct response
├── Medium → Single skill
├── High → Skill chain
└── Critical → Verification loop
↓
Reflect on approach (freedom to question, flag issues)Quick Reference
| Need | Use | Link |
|---|---|---|
| Understand routing | Intent Classification | → |
| Manage context | Context Management | → |
| Execute tasks | Skill Execution | → |
| Chain skills | Skill Chains | → |
| Structure work | PAUL Methodology | → |
| Model-specific edits | Model-Aware Editing | → |
| Large contexts | Subagents | → |
| Verify correctness | Verification Loops | → |
Next Steps
- Getting Started — Installation and setup
- Concepts — Architecture overview
- Research — Research backing to design
- Internals — Database schema and internals