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BC deep-research

Conduct enterprise-grade research with multi-source synthesis, citation tracking, and verification. Use when user needs comprehensive analysis requiring 10+ sources, verified claims, or comparison of approaches. Triggers include "deep research", "comprehensive analysis", "research report", "compare X vs Y", or "analyze trends". Do NOT use for simple lookups, debugging, or questions answerable with 1-2 searches.

MaxMiksa/Auto-Company Agent Skills author: MaxMiksa 21 files · 7 scripts body ≈ 8 146 tokens Open the sourcegithub.com↗ analyzed 12 h ago

Conduct enterprise-grade research with multi-source synthesis, citation tracking, and verification.

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

AnalyzerSoftware developmentData and analyticstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
99
Quality 40%
86
Run on models
none yet
Process rating
C
64/100
Has gaps
Progress reporting w 2
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Risky intent intent-offensive-security reference/methodology.md:308
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    **Red Team Questions:**

Files scanned: 18. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 8146 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 64/100

  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 20 mutating operations with no state check
  • 40Execution cost. Instruction body is 8146 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 60Steps. 340 steps, 4 vague phrases
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 11 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

  • -5TODO / placeholder text left in the skill
  • -224 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +4Description says when NOT to use the skill
  • +3Description length 414: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 340 items
  • +3Output format is stated explicitly
  • +4Has examples (15 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 7 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.