SKILLEMALL.ai
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AB resumeclaw

Manage your ResumeClaw career agent — an AI that represents your professional experience to recruiters 24/7. Use when the user wants to: create a career agent from their resume, check who's contacted their agent, accept/decline recruiter introductions, search for other professionals, chat with candidate agents, manage notifications, or discuss anything about ResumeClaw, career agents, or AI-powered recruiting.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files · 1 script body ≈ 1 115 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Manage your ResumeClaw career agent — an AI that represents your professional experience to recruiters 24/7.

As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorAI and agentstype 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
96/100
safety, quality, tests
Safety 60%
99
Quality 40%
91
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    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 scripts/resumeclaw.sh:125
      Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
      resp=$(api POST /api/auth/register -d "{\"email\":\"$email\",\"password\":\"$password\",\"confirmPassword\":\"$password\",\"name\":\"$name\"}")
      quoted

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 65/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 4 mutating operations with no state check
    • 65Failures and branches. 3 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 5 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1115 tokens
    • 100Progress reporting. Reports progress

    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 413: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 5 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.