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AC k-deep-research

Systematic deep research methodology for ANY domain. 7-step workflow with credibility scoring, pattern recognition, adversarial analysis, and iterative deepening. Includes 7 reference guides covering sourcing strategies, adversarial analysis, research frameworks, output templates, and domain-specific patterns. Produces exhaustive cited reports. Battle-tested across 40+ autonomous research loops.

ClawHub Agent Skills author: rustyorb v2.0.1 11 files · 1 script body ≈ 2 248 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 58/100 · Has gaps — weak spots: inputs and preconditions, running it twice, progress reporting

ProcedureData and analyticsInfrastructureResearchtype 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
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
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.
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: 11. 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")
  • note frontmatter-key unknown frontmatter key "keywords"

Process rating: all ten parameters 58/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 2 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 85Steps. 58 steps, 2 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2248 tokens
  • low 13 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)
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 398: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 58 items
  • +3Output format is stated explicitly
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (7 of 7)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
This appears to be a legitimate deep-research skill, but it is broad, always active, and includes under-scoped local memory, file, scheduling, sub-agent, and notification behaviors that users should review first.
LLM: suspicious (high) · VirusTotal: benign · 28 May 2026