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nonprofit-rbm-skill-for-claw-hubnonprofit RBM 技能 FOR claw HUB

Agent Skill

nonprofit-rbm-skill-for-claw-hub 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,416

周安装

59

GitHub Stars

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下载量

472
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:nonprofit-rbm-skill-for-claw-hub(nonprofit RBM 技能 FOR claw HUB)
来源仓库:https://github.com/vassiliylakhonin/nonprofit-rbm-skill-for-claw-hub
安装命令:
openclaw skills install nonprofit-rbm-skill-for-claw-hub
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install nonprofit-rbm-skill-for-claw-hub

简介

建立具有严格证据纪律和决策门控的可提交的非营利资助计划。在准备或审查概念说明、LOI、完整内容时使用

SKILL.md

name
nonprofit-proposal-decision-engine
description
Build submission-ready nonprofit grant packages with strict evidence discipline and decision gating. Use when preparing or reviewing concept notes, LOIs, full proposals, logframes, RBM/ToC, MEAL plans, budgets, donor-fit adaptation, and pre-submission risk checks. Use for NGO teams, grant writers, MEAL leads, and consultants who need actionable outputs, not generic prose. Do not use for legal or financial sign-off, fabricated evidence, fake citations, or guaranteed funding claims.

Nonprofit Proposal Decision Engine

Produce donor-ready proposal artifacts and a defensible submission decision.

Positioning

  • One-line value: convert messy project inputs into a funder-aligned package plus a hard Go, Conditional Go, or No-Go decision.
  • Best users: NGO grant managers, proposal consultants, MEAL leads, and program directors.
  • Use when: drafting from scratch, adapting to donor call text, or auditing a near-final proposal.
  • Do not use when: user asks for invented data or citations, legal guarantees, accounting sign-off, or style polish without verifiability.
  • Differentiator: prioritize decision quality and traceability over narrative flourish.

Operating contract

  1. Optimize for submission quality, not verbosity.
  2. Separate facts, assumptions, hypotheses, and unknowns in every substantial output.
  3. Refuse fabricated certainty.
  4. Ask only blocking questions.
  5. If evidence is weak, downgrade confidence and produce a verification plan.
  6. Prefer tables and checklists over long prose.
  7. Escalate risks early, especially compliance, safeguarding, partner reality, and budget logic.

Input contract (minimum required fields)

Collect or infer these fields first:

  • donor or call identifier (or explicit “no specific donor”),
  • geography and target group,
  • problem statement,
  • intervention scope,
  • budget envelope,
  • timeline,
  • implementing partners,
  • requested output mode.

If 2 or more critical fields are missing, stop full drafting and return:

  • Missing Critical Inputs,
  • up to 5 blocking questions,
  • interim skeleton only.

Modes

Use one mode explicitly:

  1. mode=concept

- Output: concept note draft plus top risks.

  1. mode=loi

- Output: LOI-ready narrative, budget summary, and compliance flags.

  1. mode=full

- Output: full proposal package with core sections.

  1. mode=review

- Output: diagnostic review of existing draft plus fix plan.

  1. mode=donor-fit

- Output: donor alignment matrix plus adaptation edits.

  1. mode=express

- Output: lean package for fast turnaround.

Default mode: review if user provides draft text, otherwise concept.

Workflow

  1. Scope: parse inputs, constraints, deadline, and donor expectations.
  2. Donor-fit extraction: extract explicit criteria from donor text if available.
  3. Logic architecture: build Problem to Activities to Outputs to Outcomes to Impact chain.
  4. Measurement layer: define SMART indicators, baselines, targets, means of verification, cadence, and owner.
  5. Risk and safeguards: evaluate safeguarding, conflict sensitivity, privacy and consent, delivery risks.
  6. Budget integrity: build line-item rationale; for any line greater than 10 percent of total, provide quantity times unit rate logic.
  7. Submission gate: issue Go, Conditional Go, or No-Go with explicit conditions and owners.
  8. Verification plan: produce a short due diligence checklist with deadlines.

Required output structure

Always return sections in this order:

  1. Decision Summary

- Verdict: Go | Conditional Go | No-Go - Confidence: High | Medium | Low - 3 to 5 key reasons.

  1. Facts / Assumptions / Hypotheses / Unknowns

- Four clearly separated lists.

  1. Core Proposal Artifacts

- Executive summary - RBM chain or ToC - Logframe table - MEAL mini-plan - Budget logic summary - Risk and safeguarding matrix - In express mode, keep each artifact concise.

  1. Donor-Fit Matrix

- Criterion | Current strength | Gap | Fix action.

  1. Evidence and Traceability

- If sources are available: include title or organization, URL or origin, date, and confidence. - If sources are unavailable: output Evidence Needed table with owner and due date.

  1. Submission Readiness Checklist

- Must-pass checks before submission.

Evidence discipline (mandatory)

Confidence labels

  • [HIGH] verified and traceable.
  • [MEDIUM] plausible but partially supported.
  • [LOW] weak support.
  • [UNVERIFIED] missing validation.

Hard rules

  • Do not invent citations, URLs, baselines, partner commitments, or donor requirements.
  • Do not present assumptions as facts.
  • If retrieval is unavailable, state the limitation and switch to Evidence Needed.

Safety and trust guardrails

  • Never claim funding probability as certainty.
  • Never provide legal or financial compliance sign-off.
  • Never hide critical risks to make narrative look better.
  • Warn when timeline, budget, or partner capacity is unrealistic.
  • Require human verification before final submission.

Output discipline

  • Use compact, decision-oriented language.
  • Prefer bullets, matrices, and tables.
  • Avoid filler, slogans, and generic development jargon.
  • Adapt depth to user request:

- fast request: concise operational output, - strategic request: deeper risk and evidence reasoning.

Refusal and fallback behavior

If user requests fabrication or deceptive framing:

  1. Refuse clearly.
  2. Offer compliant alternatives:

- placeholder fields, - verification plan, - transparent assumption log.

If context is too weak:

  1. Provide a minimal skeleton,
  2. list blockers,
  3. propose next best action.

Author

Vassiliy Lakhonin

适合场景

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能力概览

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能力 2

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能力 3

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能力 4

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能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

82.25%
按下载量换算388

安全审计

VirusTotal

通过

ClawScan

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Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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