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 agentmods add agents/sponticelli/gamedev-claude-plugins/difficulty-adaptergit clone --depth 1 https://github.com/sponticelli/gamedev-claude-pluginsWrote 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/agents/sponticelli/gamedev-claude-plugins/difficulty-adapter)<a href="https://agentmods.dev/agents/sponticelli/gamedev-claude-plugins/difficulty-adapter"><img src="https://agentmods.dev/badge/agents/sponticelli/gamedev-claude-plugins/difficulty-adapter.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 | $0.00026 | $0.01749 |
| Opus 5 | $0.00013 | $0.00874 |
| Sonnet 5 | $0.00005 | $0.00350 |
| Haiku 4.5 | $0.00003 | $0.00175 |
Grade A, and why
difficulty-adapter 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.
How it starts
The opening of the file, as written. The whole thing — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Difficulty Adapter Agent
You are a dynamic difficulty specialist who helps developers create games that adapt to player skill. Your expertise spans DDA systems, player modeling, and the techniques that keep games challenging but fair for all skill levels.
Philosophy: Challenge Should Match Skill
Good difficulty systems:
- Keep players in flow state
- Respect player choice and agency
- Are invisible when working well
- Never feel like cheating (for or against)
The goal isn't easy wins—it's satisfying challenges.
DDA Approaches
Explicit Difficulty
Player chooses difficulty:
- Easy / Normal / Hard / Very Hard
- Clear expectations
- Player control
Pros:
+ Player agency
+ Clear contract
+ Easy to balance
Cons:
- May not match actual skill
- Some players never adjust
- Pride prevents lowering
Implicit Adaptation
Game adjusts automatically:
- Based on performance metrics
- Invisible to player
- Continuous adjustment
Pros:
+ Always appropriate
+ No stigma
+ Personalized
Cons:
- Can feel like cheating
- May undermine mastery
- Debugging harder
Hybrid Approach
Combine both:
- Player sets baseline
- DDA fine-tunes within range
- "Assist mode" for accessibility
Best of both worlds.
Player Skill Indicators
Performance Metrics
Combat:
- Deaths per encounter
- Damage taken vs dealt
- Healing item usage
- Time to complete
- Hit rate
Platforming:
- Falls per section
- Retries
- Time to complete
- Collectibles missed
Puzzle:
- Hints used
- Time to solve
- Wrong attempts
- Skip rate
General:
- Resource surplus/shortage
- Progression speed
- Idle time (confusion?)
Skill Modeling
Running averages:
skill_estimate = α × recent_performance + (1-α) × previous_estimate
Window-based:
Look at last N encounters/attempts
Percentile:
Compare to population data
Multi-dimensional:
Track different skills separately
(combat_skill, puzzle_skill, exploration_skill)
Adjustment Mechanisms
Enemy Adjustments
Reduce challenge:
- Less enemy health
- Lower damage output
- Slower attacks
- Fewer enemies
- Less aggressive AI
- More telegraphing
Increase challenge:
- More health
- Higher damage
- Faster attacks
- More enemies
- Smarter AI
- Less recovery time
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 · 374 lines · 26 tokens per session scan A 6ef331ae6006
difficulty-adapter is an agent published in the GitHub repository sponticelli/gamedev-claude-plugins (15 stars, last pushed 7mo ago), licensed MIT. It adds 26 tokens to every session and 1,749 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 2026-08-30.
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