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 codebygarv/Ai-skills --skill second-order-effects-analyzergit clone --depth 1 https://github.com/codebygarv/Ai-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/codebygarv/ai-skills/second-order-effects-analyzer)<a href="https://agentmods.dev/skills/codebygarv/ai-skills/second-order-effects-analyzer"><img src="https://agentmods.dev/badge/skills/codebygarv/ai-skills/second-order-effects-analyzer.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00023 | $0.00352 |
| Opus 5 | $0.00012 | $0.00176 |
| Sonnet 5 | $0.00005 | $0.00070 |
| Haiku 4.5 | $0.00002 | $0.00035 |
Grade A, and why
second-order-effects-analyzer 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 4d 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.
What it actually says
Purpose
Anticipate the multi-order ripple effects ("and then what?") of architectural changes, API deprecations, schema updates, or rate limit adjustments across interrelated systems, teams, and user habits.
When to Use
- Proposing a breaking change or deprecation.
- Adding strict rate limits, database indexes, or caching layers.
- Changing pricing tiers, billing models, or developer API quotas.
What to Analyze
- First-Order Effect (Immediate Intent): What does this change directly accomplish?
- Second-Order Effects (Downstream Systems): How do upstream callers, batch jobs, and databases react?
- Third-Order Effects (Human Behavior & Gaming): How will external users or internal developers change their usage habits to bypass or adapt to this change?
- Feedback Loops: Could this change trigger an amplifying feedback loop (e.g. retries worsening server outage)?
- Mitigation Strategy: Controls to prevent adverse systemic side effects.
Output Format
- Causal Chain Diagram (Mermaid): Visual flow of 1st $ ightarrow$ 2nd $ ightarrow$ 3rd order impacts.
- Impact Matrix: System/Team impacted, Expected Ripple Effect, Risk Level, Mitigation.
- Adverse Feedback Loop Warnings: Highlighting self-amplifying system traps.
Avoid
- Stopping analysis at the first-order happy path ("This cache will reduce DB load").
- Ignoring human behavioral changes in response to technical restrictions.
What ships with it
2 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.
- 4d ago First seen · 34 lines · 23 tokens per session scan A 20ce65258837
second-order-effects-analyzer is a skill published in the GitHub repository codebygarv/Ai-skills (25 stars, last pushed 19d ago), licensed MIT. It adds 23 tokens to every session and 352 once invoked, about $0.0001 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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