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BC Engineering Manager OS

Complete engineering management system — team building, 1:1s, performance, hiring, architecture decisions, incident management, and scaling. From IC-to-manager transition through director-level operations.

ClawHub Agent Skills author: 1kalin v1.0.0 3 files body ≈ 8 204 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 56/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

GeneratorInfrastructurePeople and hiringtype 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
B
82/100
safety, quality, tests
Safety 60%
100
Quality 40%
55
Run on models
none yet
Process rating
C
56/100
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.
  2. 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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 8204 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 56/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 20 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 40Consistency. Frontmatter name (Engineering Manager OS) differs from the folder (afrexai-engineering-manager)
  • 40Execution cost. Instruction body is 8204 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 70Failures and branches. 9 branches
  • 85Steps. 151 steps, 2 vague phrases
  • 100Tools and files. No external tools needed
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • 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)
  • +3Output format is not stated: the model decides each time
  • -212 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 205: enough signal without eating the budget
  • +4Structure: 55 headings
  • +3Step-by-step instructions: 151 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 55.

External checks

ClawHub: clean
This is an instruction-only engineering management playbook, with the main caution being careful handling of employee-related notes and decisions.
LLM: benign (high) · VirusTotal: benign · 28 May 2026