CC funding-trend-forecaster
Predict funding trend shifts using NLP analysis of grant abstracts from NIH, NSF, and Horizon Europe
Predict funding trend shifts using NLP analysis of grant abstracts from NIH, NSF, and Horizon Europe
As a process C 62/100 · Has gaps — weak spots: when it triggers, running it twice, progress reporting
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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 · 0
✓ No critical or high findings
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 100 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - note
frontmatter-keyunknown frontmatter key "status" - note
frontmatter-keyunknown frontmatter key "risk_level" - note
frontmatter-keyunknown frontmatter key "skill_type" - note
frontmatter-keyunknown frontmatter key "owner" - note
frontmatter-keyunknown frontmatter key "reviewer" - note
frontmatter-keyunknown frontmatter key "last_updated"
Process rating: all ten parameters 62/100
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 1 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 36 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2030 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 17 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)
- +3Description length 100: 120–800 characters recommended
- +2Single-language instructions
- +4Structure: 23 headings
- +3Step-by-step instructions: 36 items
- +3Output format is stated explicitly
- +4Has examples (8 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.
External checks
ClawHub: suspicious
The skill does not show theft or persistence, but it can present synthetic grant forecasts as real analysis and has avoidable install-time supply-chain risk.
LLM: suspicious (high) · VirusTotal: · 14 Sept 2026