SKILLEMALL.ai

AC long-research

[BETA] Deep research that actually reads pages instead of summarizing search results. Tell it how long to research (10 min, 2 hours, all night) and it works the full duration — searching, reading every result, following leads, cracking forums, cross-verifying findings, and writing progressively to a research file. Tree-style exploration: each page read spawns new searches, like a human researcher. Enforced read-to-search ratio prevents shallow search-spamming. Wall-clock time commitment — it won't finish early. Self-audit gate blocks delivery until quality checks pass. Works with web_search, web_fetch, and browser-use for JS-heavy sites.

ClawHub Agent Skills author: vanya1210 v1.2.0 4 files body ≈ 9 527 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 64/100 · Has gaps — weak spots: inputs and preconditions, execution cost, running it twice

ProcedureAI and agentsWriting and documentsData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
C
64/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
Execution cost w 6
40
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: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 9527 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "dependencies"

Process rating: all ten parameters 64/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 10 mutating operations with no state check
  • 40Execution cost. Instruction body is 9527 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 208 steps
  • 100Failures and branches. 22 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 15 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)
  • -267 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 645: enough signal without eating the budget
  • +4Structure: 48 headings
  • +3Step-by-step instructions: 208 items
  • +3Output format is stated explicitly
  • +4Has examples (15 code blocks)
  • +4Reference files are cited in the instructions (2 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: suspicious
This research skill is transparent about its purpose, but it needs review because it can run long autonomous browser research using stored browser sessions, login-gated pages, and remote browser infrastructure.
LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026