SKILLEMALL.ai
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AC bug-fixing

Zero-regression bug fix workflow with self-evolution: reproduce → root cause → scope → impact prediction → fix → regression verify → code review → self-reflect & evolve. Use when: - Feature broken, incorrect behavior, wrong output, errors/exceptions - Console errors/warnings even when "working" - Regressions, timeouts, degraded performance - "fix bug", "debug", "not working", "error", "broken" Output: Bug summary + verification report + code review + self-reflection score. Not for: new features (fullstack-developer); pure review (code-review); optimization (performance-optimization)

modbender/skill-library-mcp Agent Skills author: modbender MIT 26 files body ≈ 3 852 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Zero-regression bug fix workflow with self-evolution: reproduce → root cause → scope → impact prediction → fix → regression verify → code review →…

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureData and analyticsSoftware developmenttype 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%
98
Quality 40%
92
Run on models
none yet
Process rating
C
54/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Risky intent intent-offensive-security references/cross-surface-regression.md:185
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Auth checked before actions | No privilege escalation |
    • low Risky intent intent-offensive-security references/scope-accuracy-protocol.md:88
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | **Security invariants** | Auth checked before actions, no privilege escalation |

    Files scanned: 26. 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 54/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 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
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 26 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3852 tokens
    • low 16 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

    • +3Output format is not stated: the model decides each time
    • -250 emoji in the instructions: noise for the model
    • -33 of 3 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 591: enough signal without eating the budget
    • +4Structure: 36 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (21 of 21)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.