Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
ADI Decision Engine
Structured multi-criteria decision analysis for ranking options with weights, constraints, confidence, tradeoff reasoning, sensitivity analysis, and explaina...
skill openclawclawhub Free
Structured multi-criteria decision analysis for ranking options with weights, constraints, confidence, tradeoff reasoning, sensitivity analysis, and explaina...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, agents/openai.yaml, assets/icon-large.svg, assets/icon-small.svg, examples/hiring_shortlist.json, examples/research_methods.json
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- dimgouso
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Core promise
Turn a messy tradeoff problem into a structured, auditable multi-criteria decision and return a ranked recommendation with confidence and explanation.
When to use this skill
Use this skill when the user needs structured decision support rather than open-ended brainstorming. Typical triggers include: multi-criteria decision analysis weighted scoring or option ranking vendor selection or procurement route planning with explicit tradeoffs hiring shortlist ranking tool or platform comparison policy-driven or auditable agent decisions
Input modes
This skill supports exactly two input modes.
1. Structured mode
The user already has a decision request with: options criteria optional constraints optional policy_name optional evidence, confidence, or context Use scripts/validate_request.py first if request quality is uncertain, then scripts/run_adi.py to execute it.
2. Freeform mode
The user provides a natural-language tradeoff problem. First use scripts/normalize_problem.py to produce a request skeleton. Do not pretend the request is complete if important fields are missing. If the skeleton is not ready, ask for the missing inputs instead of inventing scores or constraints.
Output contract
If ADI runs successfully, the final answer must contain: best_option a short rationale for why it won top-ranked alternatives confidence summary constraint impact summary sensitivity or stability summary when available explicit assumptions If the request is not complete enough to run, return a request-completion prompt rather than a fabricated ranking.
Workflow
Determine whether the user input is structured or freeform. For freeform input, normalize it into a request skeleton using scripts/normalize_problem.py. Validate candidate requests with scripts/validate_request.py. Run complete requests with scripts/run_adi.py. Present the ADI result in clear decision-support language: recommendation first strongest tradeoff second caveats and sensitivity after that
Decision hygiene rules
Never rank options without explicit criteria. Never silently invent hard constraints. If criterion direction is ambiguous, stop and clarify. Normalize vague goals into named criteria before scoring. Prefer a small, explicit criteria set over many overlapping criteria. Keep the policy choice visible: balanced, risk_averse, or exploratory.
Output quality rules
Show the top recommendation first. Explain why it won. Mention the strongest tradeoff. Call out eliminated or constraint-violating options. Include confidence caveats when evidence is weak. Use a compact comparison table or structured bullet list when comparing several options.
Safety and honesty rules
No hidden math. No fake scores. No fabricated evidence. Do not claim ADI ran if the runtime dependency is missing. Do not request API keys. Do not require network access for the core workflow. Do not tell the user to trust the ranking if the request is under-specified.
Runtime requirements
python3 either an importable adi-decision package or the adi CLI on PATH If the ADI runtime is unavailable, stop with a clear error and explain that the dependency must be installed locally.
References
Request schema: references/request_schema.md Result interpretation: references/result_interpretation.md Policy guide: references/policy_guide.md Use cases: references/use_cases.md
Examples
examples/vendor_selection.json examples/route_planning.json examples/hiring_shortlist.json examples/research_methods.json examples/tool_selection.json
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Config2 Assets1 Docs
- SKILL.md
- agents/openai.yaml
- examples/hiring_shortlist.json
- examples/research_methods.json
- assets/icon-large.svg
- assets/icon-small.svg