SKILLEMALL.ai
GitHub ↗ RU Sign in

BC srl-session-wrapper

Wrap a learning session in a plan → monitor → reflect cycle. Use at the start of any substantial study session to set goals, mid-session to check strategy, and at session end to consolidate what changed. Builds self-regulated learning as a habit.

GarethManning/education-agent-skills Claude Code author: GarethManning 1 file body ≈ 3 647 tokens Open the sourcegithub.com↗ analyzed 9 h ago

Wrap a learning session in a plan → monitor → reflect cycle.

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorAI and agentsPersonal productivitytype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
53/100
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 · 0

    ✓ No critical or high findings

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "effort"
    • note frontmatter-key unknown frontmatter key "skill_id"
    • note frontmatter-key unknown frontmatter key "skill_name"
    • note frontmatter-key unknown frontmatter key "domain"
    • note frontmatter-key unknown frontmatter key "domain_number"
    • note frontmatter-key unknown frontmatter key "audience"
    • note frontmatter-key unknown frontmatter key "evidence_strength"
    • note frontmatter-key unknown frontmatter key "evidence_sources"
    • note frontmatter-key unknown frontmatter key "input_schema"
    • note frontmatter-key unknown frontmatter key "evidence_captured"
    • note frontmatter-key unknown frontmatter key "chains_well_with"

    Process rating: all ten parameters 53/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 4 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3647 tokens
    • 100Running it twice. No mutating operations

    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 246: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 4 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.