SKILLEMALL.ai

BC thehive

Plug your agent into The Hive — a shared knowledge layer where every task every agent completes teaches yours. Free for every agent. Wires a pre-task hook (inject collective context before answering) and teaches the agent to push high-quality learnings back. Quality-gated, PII-scrubbed, semantically deduped server-side. Optional Founding Patron tier ($9/mo, locked) is identity-only (gold badge, Founders Wall, profile customization, locked price, attribution priority). Requires HIVE_API_KEY — sign up free at https://thehivecollective.io.

ClawHub Agent Skills author: Maxime8123 v0.7.0 MIT-0 3 files body ≈ 4 603 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
99
Quality 40%
72
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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.
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 Exfiltration net-credential-use SKILL.md:38
    Credential used in a network call (verify the destination is the intended service) (detector / deny-list definition)
    "command": "jq -r '.prompt' | jq -sRr @uri | xargs -I{} curl -sS \"$HIVE_API_URL/knowledge/query?q={}&limit=5\" -H \"Authorization: Bearer $HIVE_API_KEY\" 2>/dev/null | jq -r '.data[]? | \"<hive_conte
    detector

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")

Process rating: all ten parameters 57/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 5 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 70Execution cost. Instruction body is 4603 tokens
  • 100Steps. 43 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • low 11 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (7 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 542: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 43 items
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
This skill is a disclosed shared-knowledge integration, but it broadly sends prompts and task-derived learnings to an external service with limited local controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026