Borrowing it
Nothing to install: this file belongs to different-ai/agent-bank. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/different-ai/agent-bank/main/.opencode/skill/skill-reinforcement/SKILL.mdgit clone --depth 1 https://github.com/different-ai/agent-bankWrote 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/different-ai/agent-bank/skill-reinforcement)<a href="https://agentmods.dev/skills/different-ai/agent-bank/skill-reinforcement"><img src="https://agentmods.dev/badge/skills/different-ai/agent-bank/skill-reinforcement/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/different-ai/agent-bank/skill-reinforcement"><img src="https://agentmods.dev/badge/skills/different-ai/agent-bank/skill-reinforcement.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00021 | $0.01852 |
| Opus 5 | $0.00010 | $0.00926 |
| Sonnet 5 | $0.00004 | $0.00370 |
| Haiku 4.5 | $0.00002 | $0.00185 |
Grade A, and why
skill-reinforcement scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. API format is wrong (like `-d` vs `-F` for curl) How it starts
The opening of the file, as written. The whole thing — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What I Do
After ANY skill is used, this meta-skill triggers to:
- Analyze what worked and what didn't
- Identify new patterns or shortcuts discovered
- Update the skill file with learnings
- Prevent knowledge loss between sessions
Relationship with self-improve:
- This skill (
skill-reinforcement) = WHEN to update (post-use triggers) - The
self-improveskill = HOW to update (templates, structures, decision trees)
When to Trigger
Invoke this skill automatically when:
- Any skill from
.opencode/skill/*/SKILL.mdcompletes - A workflow succeeds or fails in a notable way
- New shortcuts or anti-patterns are discovered
- Token usage could be reduced with better patterns
- API behavior differs from documentation
- Commands fail and I find the fix
- User confirms something works
- I do the same task twice (should become a skill/tool)
Reinforcement Process
Step 1: Capture the Context
After using a skill, note:
- Skill used: [skill-name]
- Task: [what was being done]
- Outcome: [success/partial/failure]
- Token cost: [high/medium/low]
- Time taken: [fast/normal/slow]
Step 2: Identify Learnings
Ask these questions:
- What took longer than expected? → Document the fix
- What failed unexpectedly? → Add to "Common Issues"
- What shortcut was discovered? → Add to "Token Saving Tips"
- What assumption was wrong? → Correct in documentation
- What worked better than documented? → Update the workflow
Step 3: Categorize the Learning
| Category | Where to Add | Example |
|---|---|---|
| New shortcut | "Token Saving Tips" | OTP visible in email preview |
| Failure mode | "Common Issues" | Popup blocker breaks flow |
| Better pattern | Main workflow | Check login state first |
| Anti-pattern | "Anti-Patterns to Avoid" | Don't snapshot spam |
| Environment quirk | "Prerequisites" or "Notes" | Session persists |
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 · 297 lines · 21 tokens per session scan A 939fd35745d8
skill-reinforcement is a skill published in the GitHub repository different-ai/agent-bank (249 stars, last pushed 5mo ago), licensed MIT. It adds 21 tokens to every session and 1,852 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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