Meta Systems Thinking

What it does

When your agent uses it

• The user describes a multi-actor situation that resists linear fixes: policy interventions that backfire
• Org-level fixes that break other teams
• Market symptoms that return after being solved
• Time-lagged second-order consequences

Example prompts

• “why does fixing X make Y worse”
• “identify the leverage points in this system”
• “this keeps coming back”
• “/meta-systems-thinking”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Define the system boundary: What's in, what's out?
2. 2Map key variables: What are the important stocks (quantities that accumulate)?
3. 3Identify feedback loops: Which loops are reinforcing? Which are balancing?
4. 4Find delays: Where is cause separated from effect in time?
5. 5Locate leverage points: Where would small interventions produce the biggest shift?
6. 6Check for unintended consequences: What might this intervention break elsewhere in the system?

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Meta Systems Thinking loads about 1.2k tokens when it runs, and up to ~5.1k if it reads all its reference files. Until then it costs about 209 tokens; SKILL.md has 314 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~209

When it runs · the whole SKILL.md, loaded when a task matches

~1.2k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~5.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 314 words, ~1,152 tokens.

name

meta-systems-thinking

description

Apply systems thinking — causal loop diagrams, stock-and-flow models, system archetypes, and leverage-point analysis — to organizational, economic, or social problems where feedback loops, delays, or emergent behavior drive recurring failure across multiple interacting actors. Use this skill when the user describes a multi-actor situation that resists linear fixes: policy interventions that backfire, org-level fixes that break other teams, market symptoms that return after being solved, or time-lagged second-order consequences, even if they say 'why does fixing X make Y worse' or 'identify the leverage points in this system'. Do NOT use for single-cause software bugs, flaky tests, or regressions — those are debugging problems, not systems-thinking problems, even when phrased as 'this keeps coming back'.

metadata.category

WP-22 跨學科

metadata.tags

meta-thinking, systems-thinking, complexity

IRON LAW: First-Order Fixes in Complex Systems Produce Second-Order
Backlash Within 2 Cycles — Map the Feedback Loop BEFORE Intervening

Agents default to "fix the symptom directly" (e.g., high turnover → raise
salaries). In systems with feedback loops, the direct fix triggers a
compensating response that makes the original problem worse OR creates
a new one (raise salaries → budget squeeze → cut training → worse
onboarding → higher turnover). Before recommending any intervention,
draw the causal loop diagram and identify at least one reinforcing and
one balancing loop. If you can't find any, the problem may not be a
systems problem — don't force the framework.

# Systems Analysis: {Problem}

## System Boundary
- In scope: ...
- Out of scope: ...

## Key Variables
- {Variable A}: {description}

## Feedback Loops
- Reinforcing: {A → B → A (amplifying)}
- Balancing: {A → B → C → opposes A (stabilizing)}

## Delays
- {Input} → {Effect} (delay: {timeframe})

## Leverage Points
1. {where small change = big impact}

## Unintended Consequences Risk
- If we {intervention}, it might also {side effect} because {loop/connection}

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in meta-systems-thinking of asgard-ai-platform/skills.

• SKILL.md
• examples/sample_scenario.md
• references/system-archetypes.md

Open the folder on GitHubat commit 4e7f4f8