Context

An Evolution Agent iteratively improves a NexAU coding agent's configuration to maximize scores on Terminal Bench (a coding benchmark). You must provide it with concrete, specific, implementable knowledge.

The Evolution Agent has NO pre-existing knowledge about coding agent architectures or SOTA techniques. Your output will be its sole reference for understanding what top coding agents do and how to replicate their approaches. You must provide:

1. Architecture & design patterns: component blueprints, constraint hierarchies, gap analysis frameworks from top teams
2. Exact numbers: scores, params, thresholds, token counts, timing data
3. Actual code and config: real system prompts, middleware code, tool definitions — not just design principles
4. Ablation data: which technique contributed how many percentage points
5. Latest developments: new teams, new scores, techniques from {{ date[:4] }}
6. Implementation specifics: exact compaction algorithms, exact retry counts, exact prompt text
7. Failure mode analysis: what top teams tried and FAILED (negative results are as valuable as positive ones)

Be comprehensive. Cover both high-level design principles AND concrete implementation details. Focus on ACTIONABLE FACTS and EXACT DATA.

Output Directory (WRITE)

You must produce ONE skill file:

1. {{ output_skill_dir }}/coding-agent-sota-research/SKILL.md — architecture, benchmarks, techniques

⚠️ CRITICAL RULES

1. WRITE EARLY, UPDATE OFTEN. Write the skill file after reading the first batch of URLs. Then update it as you discover more information.
2. Record EXACT data — reject vague summaries.
   • ✅ "deepagents scored 66.5% on TB2 using GPT-4.1 with 300 max iterations"
   • ❌ "deepagents scored well on terminal bench"
   • ✅ "compaction keeps last 15 messages, summarizes older ones into 5 sentences using gpt-4.1-mini"
   • ❌ "uses context management with sliding window"
3. Cite every claim. Include the source URL for every data point.
4. Prioritize implementable details over architectural summaries.
5. Use {{ date }} year in search queries for recent results.

Your Research Protocol

Phase 1: Read Pre-given URLs (MANDATORY)

{% for source in web_sources %}

• {{ source.url }} Focus: {{ source.focus }} {% endfor %}

For each URL:

1. Use WebFetch to read the full page
2. Extract ALL concrete technical details — focus on EXACT numbers, configs, code snippets, and ablation results
3. Ignore high-level architecture summaries (already known) — dig for specifics
4. Record the URL as source citation

🔒 After reading all pre-given URLs: WRITE the skill file immediately. Include whatever you have so far. You will expand it in Phase 2.

Phase 2: Autonomous Deep Research (expand the skill file)

Search for MORE information. Target: 15-20 web searches total.

Architecture & Techniques (→ coding-agent-sota-research)

1. "terminal bench 2 leaderboard {{ date[:4] }} scores" — exact scores, model choices, dates
2. "deepagents terminal bench middleware code" — actual middleware implementation
3. "coding agent system prompt template {{ date[:4] }}" — actual prompt text from top agents
4. "coding agent context compaction algorithm implementation" — exact algorithms
5. "coding agent pre-completion verification middleware" — actual code
6. "SWE-agent tools file editing search replace implementation" — tool design specifics
7. "coding agent ablation study results {{ date[:4] }}" — which techniques mattered most
8. "terminal bench timeout handling strategies" — exact timeout values, fallback logic
9. "e2b sandbox coding agent optimization" — sandbox warm-up, file upload strategies
10. "coding agent doom loop detection implementation" — exact detection logic
11. "aider edit format unified diff search replace benchmark" — edit format comparison data
12. "codex cli agent architecture tools" — exact tool set and descriptions
13. "claude code hooks compaction implementation" — exact hook sequence, compaction details
14. "coding agent negative results failed techniques {{ date[:4] }}" — what didn't work and why

For each search result:

• Skip overview/summary articles — look for blog posts with code, configs, or data
• Follow links to GitHub repos, technical deep-dives, and papers with experiments
• If a page is inaccessible, note "INACCESSIBLE: <url>" and move on

🔒 After completing research: UPDATE the skill file with all findings, then call complete_task.

Skill Output Specification

---

coding-agent-sota-research/SKILL.md

Must cover the following — with BOTH design patterns AND exact data:

§1. Leaderboard Data (exact numbers required)

For each top agent/team (aim for 10+):

Agent	TB2 Score	Model	Max Iterations	Context Window	Date	Source
deepagents	66.5%	GPT-4.1	???	???	2025-XX	URL
...	...	...	...	...	...	...

Also include: score progression history, SWE-bench scores if available.

§2. Concrete Implementation Details (one subsection per top team)

For EACH top team, document SPECIFICS (not design philosophy):

Example of what we need for each team:

• Exact system prompt (copy verbatim if available, or quote key sections)
• Exact tool definitions (tool names, parameter schemas, description text)
• Exact middleware configs (param values: max_iterations=300, threshold=0.75, etc.)
• Exact compaction algorithm (e.g., "keeps last 15 messages as-is, summarizes messages 0-N into a single message using prompt: '...'")
• Exact retry logic (e.g., "retries 3 times with 2s/4s/8s backoff on status 429, 500, 502")
• Exact loop detection (e.g., "tracks {tool_name + first_arg: count}, injects warning at count=4")
• Exact pre-completion check (e.g., "intercepts complete_task, injects message: 'Before completing, verify: (1)... (2)... (3)...'")

§3. Technique Ablation Data (measured impact required)

For each technique, document the MEASURED impact:

Technique	Team	Impact	Baseline	With Technique	Source
Pre-completion checklist	LangChain	+X.X%	??%	??%	URL
Loop detection	LangChain	+X.X%	??%	??%	URL
Context compaction	???	+X.X%	??%	??%	URL
...	...	...	...	...	...

If exact ablation numbers aren't available, note "NO ABLATION DATA" and provide the team's qualitative assessment.

§4. Actual Code & Config Examples

Collect REAL code and config from open-source agents:

• System prompt text (verbatim quotes, as long as needed)
• Middleware implementations (actual Python code)
• Tool YAML definitions (actual schemas)
• Agent config files (actual YAML)

For each code example:

Source: [repo URL + file path]
Context: [what this code does in the agent pipeline]

§5. Negative Results & Failed Techniques

What did top teams try that DIDN'T work?

• Techniques that were attempted and rolled back
• Ablations showing certain changes hurt performance
• Common pitfalls documented by teams

§6. Architecture Patterns & Design Principles

Synthesize the common patterns across top teams:

• Component blueprint: What categories of components do top agents have? (e.g., Environment Onboarding, Planning, Tool Quality, Anti-Loop, Self-Verification, Time Budget, Context Management, Progressive Disclosure)
• Constraint hierarchy: Which enforcement mechanisms are strongest? (e.g., tool_impl > middleware > tool_desc > skill > system_prompt)
• Gap analysis: How to identify what's missing in an agent harness — map failure patterns to component categories, classify as PATCH vs CREATE.
• Design principles: What general rules do top teams follow when building agent harnesses? (e.g., mechanisms over rules, deterministic over advisory)

§7. Actionable Recommendations (with implementation specifics)

Top 10 concrete improvements, each with:

• What: Exact description of the change
• Why: Evidence from research (cite specific scores/ablations)
• How (in NexAU): Which file to modify, what code to write, what config to set
• Expected impact: Based on published data
• Risk: What could go wrong, based on negative results

Target length: 400-800 lines.

Format

---
name: coding-agent-sota-research
description: >-
  Comprehensive coding agent SOTA reference from top teams.
  Contains architecture patterns, component blueprints, exact benchmark
  scores, actual system prompts, middleware code, tool configs, ablation
  results, and negative results.
  This is the Evolution Agent's sole SOTA architecture knowledge source.
  Auto-generated by explore-agent (web research).
---

# Coding Agent SOTA Research
...

---

Quality Criteria

The skill file MUST:

1. Start with valid YAML frontmatter
2. Cite source URLs for every factual claim
3. Include exact numbers — NO vague descriptions
4. Include actual code/config snippets from real agents (not fabricated)
5. Flag uncertainty: "UNVERIFIED: ..." or "NO DATA" for unconfirmed claims
6. Cover both high-level design patterns AND concrete implementation details
7. Be directly implementable: an Evolution Agent should be able to copy configs/code from this skill

When done, call complete_task.