Nonprofit RBM Skill For Claw Hub
by @vassiliylakhonin
Build submission-ready nonprofit grant packages with strict evidence discipline and decision gating. Use when preparing or reviewing concept notes, LOIs, ful...
clawhub install nonprofit-rbm-skill-for-claw-hubπ About This Skill
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
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:
If 2 or more critical fields are missing, stop full drafting and return:
Missing Critical Inputs,Modes
Use one mode explicitly:
1. mode=concept
- Output: concept note draft plus top risks.
2. mode=loi
- Output: LOI-ready narrative, budget summary, and compliance flags.
3. mode=full
- Output: full proposal package with core sections.
4. mode=review
- Output: diagnostic review of existing draft plus fix plan.
5. mode=donor-fit
- Output: donor alignment matrix plus adaptation edits.
6. 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.
2. Facts / Assumptions / Hypotheses / Unknowns
- Four clearly separated lists.
3. 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.
4. Donor-Fit Matrix
- Criterion | Current strength | Gap | Fix action.
5. 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.
6. 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
Evidence Needed.Safety and trust guardrails
Output discipline
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