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 skills/d-o-hub/github-template-ai-agents/iterative-refinementnpx skills add d-o-hub/github-template-ai-agents --skill iterative-refinementgit clone --depth 1 https://github.com/d-o-hub/github-template-ai-agentsWrote 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/d-o-hub/github-template-ai-agents/iterative-refinement)<a href="https://agentmods.dev/skills/d-o-hub/github-template-ai-agents/iterative-refinement"><img src="https://agentmods.dev/badge/skills/d-o-hub/github-template-ai-agents/iterative-refinement.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 | $0.00071 | $0.01561 |
| Opus 5 | $0.00036 | $0.00781 |
| Sonnet 5 | $0.00014 | $0.00312 |
| Haiku 4.5 | $0.00007 | $0.00156 |
Grade A, and why
iterative-refinement 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 3d 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Iterative Refinement
Execute workflows iteratively with systematic validation, progress tracking, and intelligent termination.
When to Use
Use for tasks requiring iterative refinement:
- Test-fix-validate cycles: Fix failures → retest → repeat until passing
- Code quality improvement: Review → fix → review until standards met
- Performance optimization: Profile → optimize → measure until targets achieved
- Progressive enhancement: Iterative improvements until diminishing returns
Don't use for single-pass tasks, purely parallel work, or simple linear workflows.
Pre-Usage Research (Optional)
Before starting iterations, consider researching current best practices, known issues, optimal configurations, or recent alternatives for your validation tools.
Research first when: unfamiliar tools, new tech stack, complex quality criteria, or high-stakes optimization.
Core Loop Pattern
Every iteration follows:
- Execute action (fix, optimize, improve)
- Validate result (test, measure, check)
- Assess progress (compare to criteria)
- Decide (continue or stop)
Instructions
Step 1: Define Configuration
Establish before starting:
Success Criteria (specific and measurable):
- Criterion 1: [Example: "All 50 tests passing"]
- Criterion 2: [Example: "Zero linter warnings"]
- Criterion 3: [Example: "Response time < 100ms"]
Loop Limits:
- Max iterations: 5-15 (justify if >20)
- Min iterations: (optional)
Termination Mode:
- Fixed: Run exactly N iterations
- Criteria: Stop when success criteria met
- Convergence: Stop when improvements < threshold (e.g., <10% over 3 iterations)
- Hybrid: Combine multiple conditions
Step 2: Execute Iteration
For each iteration:
-
Take action - Apply fixes or implement changes
-
Run validator - Execute tests, linters, or measurements
-
Record progress:
Iteration N: - Action: [what was done] - Results: [metrics/outcomes] - Issues remaining: [count/description] - Decision: [Continue/Success/Stop]
What ships with it
6 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.
- 3d ago First seen · 248 lines · 71 tokens per session scan A a932d8e843f6
iterative-refinement is a skill published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed today), licensed MIT. It adds 71 tokens to every session and 1,561 once invoked, about $0.0004 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 2026-08-31.
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