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BC fundraiseup

Interact with FundraiseUp REST API to manage donations, recurring plans, supporters, campaigns, and donor portal access. Process online and offline donations, retrieve fundraising analytics, and integrate with nonprofit CRM systems.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 3 files body ≈ 5 619 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Interact with FundraiseUp REST API to manage donations, recurring plans, supporters, campaigns, and donor portal access.

As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

IntegrationStripeSoftware developmentMarketingtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
85/100
safety, quality, tests
Safety 60%
99
Quality 40%
63
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token SKILL.md:30
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    ```FUNDRAISEUP_API_KEY ```- API Key (e.g., ```ABED…clA```)
    quoted

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5619 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 54/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 22 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5619 tokens
  • 85Steps. 227 steps, 2 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 18 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 232: enough signal without eating the budget
  • +4Structure: 55 headings
  • +3Step-by-step instructions: 227 items
  • +4Has examples (24 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.