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AC pmp-agentclaw

AI project management assistant for planning, tracking, and managing projects using industry-standard methodologies. Use when asked to plan projects, track schedules, manage risks, calculate earned value, run sprints, create WBS, generate status reports, assign RACI responsibilities, or perform any project management task. Supports predictive (waterfall), adaptive (agile), and hybrid approaches.

modbender/skill-library-mcp Claude Code author: modbender MIT 42 files body ≈ 1 678 tokens Open the sourcegithub.com↗ analyzed 2 d ago

AI project management assistant for planning, tracking, and managing projects using industry-standard methodologies.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorOperations and projectsData and analyticsResearchtype 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%
95
Quality 40%
82
Run on models
none yet
Process rating
C
51/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
When it triggers w 12
20
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token package-lock.json:90
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…VHO+q2xB…AGd+Kl0mmq/MprG…MzA==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:134
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…VHO+q2xB…AGd+Kl0mmq/MprG…MzA==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:311
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "integrity": "sha5…ywo+qwL+oL8H…C1U+vRfLQDvw==",
      quoted
    • low Secrets in code secret-high-entropy-token package-lock.json:327
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha512-Yqfm+XDx0+Prh3…1yC+JWZ2…IL7+vK+Clp7…D7g==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:340
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…xZl+RoGR…fbT/ZgrF…0EA==",
      detector

    Files scanned: 42. 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 51/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 51 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1678 tokens
    • 100Progress reporting. Reports progress
    • 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

    • +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
    • -213 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 398: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 51 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.