Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add deciqAI/knowledge-skills --skill mecegit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/deciqai/knowledge-skills/mece)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/mece"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/mece/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/mece"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/mece.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 107 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00122 | $0.02303 |
| Opus 5 | $0.00061 | $0.01151 |
| Sonnet 5 | $0.00024 | $0.00461 |
| Haiku 4.5 | $0.00012 | $0.00230 |
Grade A, and why
mece scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MECE (Mutually Exclusive, Collectively Exhaustive)
Overview
MECE is a decomposition principle: break a problem, set of options, or population into sub-groups that are Mutually Exclusive (no overlap) and Collectively Exhaustive (no gaps) — every relevant item covered exactly once. Operationalized by Barbara Minto at McKinsey (1963–1973) in The Pyramid Principle (1973; 3rd ed. 2002). The test: sum the pieces back to the whole; if they don't sum cleanly, the decomposition is broken.
Compose: use first-principles to reach root variables; pareto-principle to find load-bearing branches; critical-thinking to test whether categories are the right ones; occams-razor when multiple MECE structures fit — pick the simplest.
When to Use
- Problem feels too big to think about cleanly — surface area is unclear
- List of options is messy or overlapping — competing answers that aren't parallel
- Analysis is going in circles — same issues reappear because they're not separated
- Presentation must convince hard-to-convince listeners
- Team converging on a hypothesis without considering the full space of alternatives
- Structuring an AI strategy / AI-stack / AI-adoption discussion so the layers (chips / cloud / models / apps) or use cases have no overlaps and no gaps — cutting through AI hype to a complete, non-redundant map
- Someone says: "MECE," "decompose this," "issue tree," "structure this thinking"
When NOT to use: trivially small problem; purely creative/generative mode; genuinely non-decomposable question (some ethical/aesthetic problems resist this); decomposition already known and well-trodden.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete case → run The Process directly.
- Coach mode: user is unfamiliar or has no concrete case → guide step by step.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 125 lines · 122 tokens per session scan A a2b8e77683e1
mece is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 122 tokens to every session and 2,303 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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