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AC master-mahasi-sayadaw

Use when user asks about 南传, 上座部, 缅甸内观, Mahasi Method, 标记法, Noting Method, 腹部起伏, 毗婆舍那, vipassanā, 四念处, 七清净, 十六观智, 刹那定, 行舍智, 马哈希尊者, Mahasi Sayadaw, Mahasi Sasana Yeiktha, IMS, or wants teaching in 马哈希尊者 Mahāsi Sayādaw's voice. Triggers include "马哈希"、"Mahasi"、"Sayadaw"、"标记法"、"腹部起伏"、"缅甸内观"、"密集禅修"、"十六观智"、"刹那定"、"妄念太多" — invoke whenever user's question touches Burmese vipassanā / Mahasi noting method, even without explicit request.

xr843/Master-skill Agent Skills author: xr843 8 files body ≈ 2 186 tokens Open the sourcegithub.com↗ analyzed 10 h ago

Use when user asks about 南传, 上座部, 缅甸内观, Mahasi Method, 标记法, Noting Method, 腹部起伏, 毗婆舍那, vipassanā, 四念处, 七清净, 十六观智, 刹那定, 行舍智, 马哈希尊者, Mahasi Sayadaw, Mahasi…

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureLearningtype 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
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
52/100
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
This is a copy of a skill from another catalog; the rating counts the canonical one: master-mahasi-sayadaw (xr843/Master-skill)

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: 8. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "lineage"
    • note frontmatter-key unknown frontmatter key "dates"
    • note frontmatter-key unknown frontmatter key "ethics"
    • note frontmatter-key unknown frontmatter key "sources"
    • note frontmatter-key unknown frontmatter key "citation_format"
    • note frontmatter-key unknown frontmatter key "verified_by"
    • note frontmatter-key unknown frontmatter key "verified_at"

    Process rating: all ten parameters 52/100

    • 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
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 45 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2186 tokens
    • 100Running it twice. No mutating operations
    • low 11 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (5 tags): a typed call is more reliable

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 429: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 45 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +1License stated

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