skill-weave 0.5.0

Adaptive skill routing for multi-agent systems

🧶 Skill Weave

Routing that learns. Chains that self-correct. Zero install to try.

What makes it special: A three-stage routing pipeline (12 chains, 72 skills) that shrinks 100+ candidate skills to ~15 before any LLM call. An online learner that gets smarter every time you use it. A weaver that chains skills into DAGs with automatic parallelism detection and verification gates.

Zero required dependencies. 111 tests passing (includes v0.4 modules). Designed for production-scale skill inventories.

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🔥 Why This Exists

Multi-agent systems drown in their own skills.

Problem	Why It Hurts	How Skill Weave Fixes It
Keyword routing breaks under overlap	"deploy" vs "ssh-deploy" vs "docker-deploy" all match	4-dim weighted scoring: semantic × recency × success × cost
Static tables rot silently	Add/remove one skill, the whole map breaks	Dynamic registration + online learning from every route
Flat LLM routing burns tokens	141 skills = 141 candidates to rank. Every. Single. Time.	3-stage cascade: Tree Filter (141→15) → BM25 → LLM Re-rank

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🆚 How It Compares

Feature	Skill Weave	Keyword Match	LangChain Router	Semantic Kernel
Zero-dependency core	✅	✅	❌	❌
3-stage cascade pipeline	✅	❌	❌	❌
Online learning from outcomes	✅	❌	❌	❌
Multi-skill DAG weaving	✅	❌	❌	❌
Chinese-English synonym match	✅	❌	❌	❌
Production-scale design (100+ skills)	✅	—	—	—
Token cost per route	0–2K	0	∞ (flat)	∞ (flat)

Bottom line: Keyword matching is fast but brittle. LangChain/SK handle semantics but burn tokens on every call. Skill Weave does both — cascade filtering + semantic re-rank — with learning on top.

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🎮 Try It Now

No install. No API key. 10 seconds.

Four interactive demos: basic routing → active learning → skill weaving → multi-plan comparison.

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📦 Install

pip install skill-weave

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⚡ 30-Second Quick Start

from skill_weave import SkillRouter

router = SkillRouter()
router.register_skill("deploy",   metadata="deploy to production, handle rollback")
router.register_skill("monitor",  metadata="monitor health metrics, alert on anomalies")
router.register_skill("rollback", metadata="revert failed deployments")

results = router.route("The new deploy broke everything, we need to go back")
for r in results:
    print(f"{r.skill.name}: {r.score:.2f}")
# → deploy:   0.28
# → monitor:  0.18
# → rollback: 0.18

This is the base router — zero dependencies, pure keyword overlap scoring. It correctly picks deploy (the task is about deployment), but doesn't catch that "go back" means rollback. That's where the full 3-stage pipeline comes in — BM25 alone pushes accuracy to 69.6% (16/23), and LLM re-rank further improves it.

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🧠 The Pipeline

flowchart TD
    TASK["💬 Task: 'deploy broke, revert now'"]
    
    TASK --> L1
    
    subgraph L1["L1: Tree Filter (zero token)"]
        T1["'deploy' → infrastructure → 37 matches"]
        T2["'revert' → narrows to 4 candidates"]
        T1 --> T2
    end
    
    L1 --> L2
    
    subgraph L2["L2: BM25 Rank (<50ms)"]
        B1["Statistical scoring over 4 candidates"]
        B2["rollback: 0.68 | deploy: 0.54"]
        B1 --> B2
    end
    
    L2 --> L3
    
    subgraph L3["L3: LLM Re-rank (optional, ~1s)"]
        R1["Semantic understanding over top ~10"]
        R2["BM25: 69.6% → +LLM re-rank"]
        R1 --> R2
    end
    
    L3 --> OUTPUT["✅ rollback (score: 0.92)"]

Stage	What It Does	Token Cost	Latency
L1: Tree Filter	Hierarchy + synonym match → narrows 141→~15	0	<1ms
L2: BM25	Character 2-gram (中文) + word-level (EN) retrieval	0	<50ms
L3: LLM Re-rank	Deep semantic reasoning over ~10 candidates	~2K	~1s

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📖 API Reference

SkillRouter — Zero-dependency core

router = SkillRouter(
    alpha=0.45,    # semantic weight
    beta=0.20,     # recency weight
    gamma=0.25,    # success rate weight
    delta=0.10,    # cost weight
)
router.register_skill(name, metadata="...", tags=[...], avg_cost=1.0)
router.unregister_skill(name)
router.route(task, top_k=5, max_cost=None, tags_filter=None)  → list[RouteResult]
router.record_outcome(skill_name, success=True, cost=1.0)
router.skills  → dict[str, Skill]

SkillWeave — Production 3-stage pipeline

sw = SkillWeave(skill_dir="/path/to/skills", llm_rank_fn=my_llm_fn)
sw.route(query, top_k=5, exclude_tier3=True)  → list[dict]
sw.run_benchmark(queries, verbose=True)        → {"accuracy": 0.696, ...}
sw.stats                                       → {"total_skills": 141, ...}

FeedbackLearner — Online weight adjustment

learner = FeedbackLearner(router)
learner.route(task, explore=True)              # UCB bandit exploration
learner.record(skill_name, task, success=True,
               dimension_contributions={"semantic": 0.9, ...})
learner.stats()                                # weight changes + success rates
learner.reset()                                # restore original weights

WeavePlanner — Multi-skill DAG orchestration

planner = WeavePlanner(router)
planner.register_chain_simple("pipeline", ["fetch", "parse", "store"])
planner.register_chain("ci-cd", ["deploy", "monitor"],
    conditions={1: ("'error' in str(output)", "rollback")})
planner.plan("run the ci-cd pipeline")          → WeaveChain
planner.plan_deep("complex task", max_depth=3)  → list[list[str]]
planner.record_chain_outcome("pipeline", True)  # track chain success

annotate — Skill metadata management

from skill_weave import annotate_skill, inject_annotations, load_skill_metadata

dims = annotate_skill("path/to/SKILL.md")       # generate 4-dim metadata
inject_annotations("path/to/SKILL.md", dims)    # write into frontmatter
skills = load_skill_metadata("/skill/dir")      # scan all skill metadata

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📊 Benchmark

23-query benchmark included in the repo (benchmark/queries.json). Results verified 2026-06-05:

Metric	Value
Skills tested	141 (63 T1 + 56 T2 + 22 T3)
BM25 + TreeFilter (top-1)	16/23 = 69.6%
BM25 + TreeFilter (top-3)	17/23 = 73.9%
Pipeline stages	Tree Filter → BM25 → LLM Re-rank

# Run the benchmark — verify yourself
python -c "
from skill_weave import SkillWeave
sw = SkillWeave('/path/to/skills')
sw.run_benchmark(verbose=True)
"

The 3-stage pipeline is designed to push accuracy well beyond keyword-only matching. LLM re-rank (not included in this benchmark) provides semantic understanding for the 30% of queries where keyword overlap alone isn't enough.

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🗺️ Architecture

skill_weave/
├── router.py        SkillRouter — 4-dim weighted scoring (zero-dependency)
├── advanced.py      SkillWeave  — 3-stage pipeline + BM25 + TreeFilter
├── annotate.py      Annotation  — 4-dim metadata generation + injection
├── learner.py       Learning    — UCB bandit + gradient weight adjustment
└── weaver.py        Weaving     — DAG orchestration (chains, parallel, conditional)

benchmark/queries.json     23 real-world routing test cases
notebooks/demo.ipynb       Colab: try before you read
tests/                     20 tests, all passing

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🤝 Contributing

Skill Weave is built by FeiMing Studio — a small team of humans and AI agents building together.

We welcome contributions. Before diving in:

1. Browse the Colab demo — understand what the project does
2. Read CONTRIBUTING.md — setup, conventions, commit style
3. Open an issue — discuss before coding large changes
4. Run the tests — python tests/test_router.py && python tests/test_learner.py

We use conventional commits (feat:, fix:, docs:) and squash-merge to main.

Development Setup

git clone https://github.com/Hxh-yaoxing/skill-weave.git
cd skill-weave
pip install -e ".[dev]"
python tests/test_router.py   # 9 tests
python tests/test_learner.py  # 11 tests

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📊 Status & Roadmap

Active development. Core pipeline stable. Used daily in production.

Version	Date	Highlights
0.3.0	2026-06-05	Active learning (UCB), skill weaving (DAG), 20 tests
0.2.0	2026-06-05	3-stage pipeline, BM25, TreeFilter, annotation, benchmark
0.1.0	2026-06-05	Core SkillRouter with 4-dim weighted scoring

Up next: v0.4 — async routing + embedding backends. Full changelog →

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👥 Authors

Role	Name
Engine & Architecture	Hermes 深蓝 (@Hxh-yaoxing)
Creative Direction & Co-creation	曜行 (He Xuheng)
Initial Scaffold	Hermes 楚乔
Infrastructure	FeiMing Studio

We're real people (and agents) who iterate fast, communicate openly, and ship on weekends. If you open an issue, a human will respond.

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📄 License

MIT — use it, fork it, ship it.

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FeiMing Studio — where humans and agents build together.