name	do-web-doc-resolver
version	0.2.10
category	tool
description	Python resolver for URLs and queries into compact, LLM-ready markdown. Use this skill when fetching documentation, resolving web URLs, or building context from web sources — even if they just say "read this doc page" or "get the docs for X". Uses progressive free-first cascade with quality scoring, circuit breakers, layered routing memory, trace-based evaluation, and agent-friendly docs validation. Not for web-search-researcher, agent-browser.
license	MIT
compatibility	Python 3.10+, async/await
allowed-tools	Bash(python:\|do-wdr:) Read
metadata	{"author":"d-oit","version":"0.2.1","source":"https://github.com/d-oit/do-web-doc-resolver"}

Web Doc Resolver Skill

Resolve URLs and queries into compact, LLM-ready markdown using a progressive, free-first cascade with 2026 production patterns.

When to Use

Fetch and parse documentation from a URL
Search for technical information across the web
Build context from web sources for AI agents
Validate content against agent-docs-spec v0.3.0
Trace resolution trajectories for debugging

Quick Start

python -m scripts.resolve "https://docs.rust-lang.org/book/"
python -m scripts.resolve "Rust async programming" --profile quality --trace --json

Cascade Strategy

URL: Cache → llms.txt → Jina → Direct Fetch → Firecrawl → Mistral Browser → DuckDuckGo

Query: Cache → Exa MCP → Exa SDK → Tavily → Serper → DuckDuckGo → Mistral Web Search

Providers (12 total)

Provider	Type	Free	Latency
`llms_txt`	URL	Yes	~100ms
`jina`	URL	Yes	~300-1500ms
`exa_mcp`	Query	Yes	Varies
`duckduckgo`	Query	Yes	~2s
`direct_fetch`	URL	Yes	~500ms
`exa`, `tavily`, `serper`	Query	No	~1s
`firecrawl`, `mistral_*`	URL/Query	No	~2-3s
`docling`, `ocr`	URL	No	~3-5s

Execution Profiles

Profile	Attempts	Paid	Latency	Quality
`free`	3	0	6s	0.70
`fast`	2	1	4s	0.60
`balanced`	4-6	1-2	9-12s	0.65
`quality`	6-10	3-5	15-20s	0.55

Key Features

Structured Tool Contracts: Every provider returns ProviderResult(ok, content, error, meta) with ProviderMeta(tool, duration_ms, cache_hit, quality_score, error_type).

Layered Routing Memory: TTL-based decay (30 days), metadata filtering, recency weighting. See scripts/routing_memory.py.

Trace-Based Evaluation: Emit ResolutionTrace with TraceStep per provider. CLI: --trace --json.

Agent-Friendly Docs Validation: Validates against agent-docs-spec v0.3.0 — 10 checks across 7 categories.

Quality Scoring: Content scored 0.0-1.0. Penalties: too_short(-0.35), missing_links(-0.15), duplicate_heavy(-0.25), noisy(-0.20).

Circuit Breakers: 3 failures → 300s cooldown per provider.

Configuration

export EXA_API_KEY="" TAVILY_API_KEY="" FIRECRAWL_API_KEY="" MISTRAL_API_KEY=""
export WEB_RESOLVER_MAX_CHARS=8000 WEB_RESOLVER_MIN_CHARS=200

Output

{"source": "jina", "content": "...", "score": 0.87,
 "metrics": {"total_latency_ms": 1234, "provider_metrics": [...], "paid_usage": False},
 "trace": {"trace_id": "...", "steps": [...], "success": True}}  # when --trace

File Structure

scripts/
├── resolve.py           # Main orchestrator & CLI
├── models.py            # Data models, enums, trace types
├── providers_impl.py    # 12 provider implementations
├── quality.py           # Content quality scoring
├── docs_validation.py   # Agent-docs-spec v0.3.0 checks
├── routing_memory.py    # Layered memory with TTL decay
├── routing.py           # Budget-aware routing
├── circuit_breaker.py   # Circuit breaker patterns
├── cache_negative.py    # Negative cache
├── synthesis.py         # LLM synthesis gate
└── utils.py             # Utilities

Rationalizations

Rationalization	Reality
"I'll just use the cheapest provider for everything."	Free providers often have lower quality, latency, or reliability; cascading with quality scoring ensures acceptable results.
"Circuit breakers are overkill — errors are rare."	Without circuit breakers, a failing provider consumes budget and time on every request, degrading the entire cascade.

Red Flags

Ignoring quality scores and accepting low-quality content without validation
Disabling circuit breakers or skipping cooldown periods during provider failures
Not setting max character limits — fetching unbounded content wastes tokens

References

Topic	File
Agent-docs-spec	https://github.com/agent-ecosystem/agent-docs-spec
Memory 2026	https://mem0.ai/blog/state-of-ai-agent-memory-2026
Agent architecture	https://andriifurmanets.com/blogs/ai-agents-2026-practical-architecture-tools-memory-evals-guardrails
Web research optimization	`references/web-research-optimization.md` - Token budgets, quality scoring, caching, profile selection

Voice & Context

Default: professional + blog
Reference: voice-profiles skill for definitions and auto-detection.

name	do-web-doc-resolver
version	0.2.10
category	tool
description	Python resolver for URLs and queries into compact, LLM-ready markdown. Use this skill when fetching documentation, resolving web URLs, or building context from web sources — even if they just say "read this doc page" or "get the docs for X". Uses progressive free-first cascade with quality scoring, circuit breakers, layered routing memory, trace-based evaluation, and agent-friendly docs validation. Not for web-search-researcher, agent-browser.
license	MIT
compatibility	Python 3.10+, async/await
allowed-tools	Bash(python:\|do-wdr:) Read
metadata	{"author":"d-oit","version":"0.2.1","source":"https://github.com/d-oit/do-web-doc-resolver"}

Web Doc Resolver Skill

Resolve URLs and queries into compact, LLM-ready markdown using a progressive, free-first cascade with 2026 production patterns.

When to Use

Fetch and parse documentation from a URL
Search for technical information across the web
Build context from web sources for AI agents
Validate content against agent-docs-spec v0.3.0
Trace resolution trajectories for debugging

Quick Start

python -m scripts.resolve "https://docs.rust-lang.org/book/"
python -m scripts.resolve "Rust async programming" --profile quality --trace --json

Cascade Strategy

URL: Cache → llms.txt → Jina → Direct Fetch → Firecrawl → Mistral Browser → DuckDuckGo

Query: Cache → Exa MCP → Exa SDK → Tavily → Serper → DuckDuckGo → Mistral Web Search

Providers (12 total)

Provider	Type	Free	Latency
`llms_txt`	URL	Yes	~100ms
`jina`	URL	Yes	~300-1500ms
`exa_mcp`	Query	Yes	Varies
`duckduckgo`	Query	Yes	~2s
`direct_fetch`	URL	Yes	~500ms
`exa`, `tavily`, `serper`	Query	No	~1s
`firecrawl`, `mistral_*`	URL/Query	No	~2-3s
`docling`, `ocr`	URL	No	~3-5s

Execution Profiles

Profile	Attempts	Paid	Latency	Quality
`free`	3	0	6s	0.70
`fast`	2	1	4s	0.60
`balanced`	4-6	1-2	9-12s	0.65
`quality`	6-10	3-5	15-20s	0.55

Key Features

Structured Tool Contracts: Every provider returns ProviderResult(ok, content, error, meta) with ProviderMeta(tool, duration_ms, cache_hit, quality_score, error_type).

Layered Routing Memory: TTL-based decay (30 days), metadata filtering, recency weighting. See scripts/routing_memory.py.

Trace-Based Evaluation: Emit ResolutionTrace with TraceStep per provider. CLI: --trace --json.

Agent-Friendly Docs Validation: Validates against agent-docs-spec v0.3.0 — 10 checks across 7 categories.

Quality Scoring: Content scored 0.0-1.0. Penalties: too_short(-0.35), missing_links(-0.15), duplicate_heavy(-0.25), noisy(-0.20).

Circuit Breakers: 3 failures → 300s cooldown per provider.

Configuration

export EXA_API_KEY="" TAVILY_API_KEY="" FIRECRAWL_API_KEY="" MISTRAL_API_KEY=""
export WEB_RESOLVER_MAX_CHARS=8000 WEB_RESOLVER_MIN_CHARS=200

Output

{"source": "jina", "content": "...", "score": 0.87,
 "metrics": {"total_latency_ms": 1234, "provider_metrics": [...], "paid_usage": False},
 "trace": {"trace_id": "...", "steps": [...], "success": True}}  # when --trace

File Structure

scripts/
├── resolve.py           # Main orchestrator & CLI
├── models.py            # Data models, enums, trace types
├── providers_impl.py    # 12 provider implementations
├── quality.py           # Content quality scoring
├── docs_validation.py   # Agent-docs-spec v0.3.0 checks
├── routing_memory.py    # Layered memory with TTL decay
├── routing.py           # Budget-aware routing
├── circuit_breaker.py   # Circuit breaker patterns
├── cache_negative.py    # Negative cache
├── synthesis.py         # LLM synthesis gate
└── utils.py             # Utilities

Rationalizations

Rationalization	Reality
"I'll just use the cheapest provider for everything."	Free providers often have lower quality, latency, or reliability; cascading with quality scoring ensures acceptable results.
"Circuit breakers are overkill — errors are rare."	Without circuit breakers, a failing provider consumes budget and time on every request, degrading the entire cascade.

Red Flags

Ignoring quality scores and accepting low-quality content without validation
Disabling circuit breakers or skipping cooldown periods during provider failures
Not setting max character limits — fetching unbounded content wastes tokens

References

Topic	File
Agent-docs-spec	https://github.com/agent-ecosystem/agent-docs-spec
Memory 2026	https://mem0.ai/blog/state-of-ai-agent-memory-2026
Agent architecture	https://andriifurmanets.com/blogs/ai-agents-2026-practical-architecture-tools-memory-evals-guardrails
Web research optimization	`references/web-research-optimization.md` - Token budgets, quality scoring, caching, profile selection

Voice & Context

Default: professional + blog
Reference: voice-profiles skill for definitions and auto-detection.

do-web-doc-resolver

Web Doc Resolver Skill

When to Use

Quick Start

Cascade Strategy

Providers (12 total)

Execution Profiles

Key Features

Configuration

Output

File Structure

See Also

Rationalizations

Red Flags

References

Voice & Context

Web Doc Resolver Skill

When to Use

Quick Start

Cascade Strategy

Providers (12 total)

Execution Profiles

Key Features

Configuration

Output

File Structure

See Also

Rationalizations

Red Flags

References

Voice & Context