SKILLEMALL.ai

AC investor-search-picoclaw

Sourced investor list for any country, built for PicoClaw. Find family offices, VCs, PE firms and angels, with a source for every field and an honest word on how complete the list is. Separates real investors from the advisers who sell to them, never merges two firms without proof, keeps its files so a second run continues instead of repeating, and delivers one Excel file. Use for investor research, deal sourcing, fundraising prospect lists, investor databases, LP and family-office lead generation, or filling gaps in a list you already have.

ClawHub Agent Skills author: Fahad Farooq v1.0.1 MIT-0 7 files body ≈ 18 351 tokens Open the sourceclawhub.ai analyzed 13 h ago

Sourced investor list for any country, built for PicoClaw.

As a process C 63/100 · Has gaps — weak spots: inputs and preconditions, execution cost, running it twice

ProcedureExcelData and analyticsSales and CRMtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
63/100
Has gaps
Execution cost w 6
10
Inputs and preconditions w 11
30
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. 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 · 0

✓ No critical or high findings

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 18351 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 63/100

  • 10Execution cost. Instruction body is 18351 tokens: crowds the task out of the window
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 35 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, read, web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 85Steps. 62 steps, 1 vague phrases
  • 100Failures and branches. 6 branches, has a failure section
  • 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 14 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (26 tags): a typed call is more reliable

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)
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +2Single-language instructions
  • +3Description length 547: enough signal without eating the budget
  • +4Structure: 67 headings
  • +3Step-by-step instructions: 62 items
  • +3Output format is stated explicitly
  • +4Has examples (30 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

External checks

ClawHub: clean
This skill is a disclosed investor-research workflow that writes its own workspace files and delivers an Excel report, with shared-channel visibility as the main caution.
LLM: benign (high) · VirusTotal: · 17 Sept 2026