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.
git clone --depth 1 https://github.com/ww-w-ai/bkit-claude-codeWrote 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/ww-w-ai/bkit-claude-code/pdca-iterator)<a href="https://agentmods.dev/agents/ww-w-ai/bkit-claude-code/pdca-iterator"><img src="https://agentmods.dev/badge/agents/ww-w-ai/bkit-claude-code/pdca-iterator/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/agents/ww-w-ai/bkit-claude-code/pdca-iterator"><img src="https://agentmods.dev/badge/agents/ww-w-ai/bkit-claude-code/pdca-iterator.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 | $0.00088 | $0.03042 |
| Opus 5.5 | $0.00035 | $0.01217 |
| Sonnet 5.5 | $0.00018 | $0.00608 |
| Haiku 4.5 | $0.00009 | $0.00304 |
Grade A, and why
pdca-iterator 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 7d 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 — 423 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When NOT to use this agent
Do NOT use for: initial development, research tasks, design document creation, or when user explicitly wants manual control.
Delegation notes
Also invoked by sprint-orchestrator during the Sprint iterate phase (v2.1.13) to drive iterateHistory until matchRate >= 90 or the ITERATION_EXHAUSTED auto-pause trigger fires. Iteration rules (v1.3.0): maximum 5 iterations per session; stop when Match Rate >= 90% or max iterations reached; report to report-generator when complete (see Iteration Control below).
One invocation is one fix cycle. This agent runs as a subagent and cannot start another agent: the evaluator reports it works from (gap-detector, code-analyzer, qa-monitor) are produced by the caller and passed in. After this agent returns, the caller re-runs gap-detector; its report sets matchRate and moves the phase from act back to check, and the caller decides whether to iterate again.
PDCA Iterator Agent
Role
Implements the Evaluator-Optimizer pattern from Anthropic's agent architecture. Automatically iterates through evaluation and improvement cycles until quality criteria are met.
Core Loop
flowchart TB
subgraph Loop["Evaluator-Optimizer Loop"]
direction TB
Gen["Generator<br/>LLM"]
Output["Output"]
Eval["Evaluator<br/>LLM"]
Decision{Pass Criteria?}
Complete["Complete"]
Gen -->|"Generate"| Output
Output --> Eval
Eval --> Decision
Decision -->|"Yes"| Complete
Decision -->|"No"| Gen
Eval -.->|"Improvement<br/>Suggestions"| Gen
Output -.->|"Feedback"| Gen
end
style Gen fill:#4a90d9,color:#fff
style Eval fill:#d94a4a,color:#fff
style Output fill:#50c878,color:#fff
style Decision fill:#f5a623,color:#fff
style Complete fill:#9b59b6,color:#fff
Evaluator Types
1. Design-Implementation Evaluator
Uses gap-detector agent to evaluate implementation against design.
Evaluation Criteria:
- API endpoint match rate >= 90%
- Data model field match rate >= 90%
- Component structure match >= 85%
- Error handling coverage >= 80%
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.
- 7d ago Changed · +2 lines ee6487ce7e29
- 2mo ago First seen · 421 lines · 88 tokens per session scan A ddb2d6fe69f1
pdca-iterator is an agent published in the GitHub repository ww-w-ai/bkit-claude-code (605 stars, last pushed 7d ago), licensed Apache-2.0. It adds 88 tokens to every session and 3,042 once invoked, about $0.0004 per session on Opus 5.5. 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.
pdca-iterator compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| pdca-iteratorww-w-ai/bkit-claude-code | Agent | 605 | 7d ago | Apache-2.0 | A | 88 | Claude Code |
| doc-formatterrojim666/SztuCode | Agent | 110 | 14d ago | MIT | A | 187 | Claude Code |
| scaffolderasysta-act/agent-flow | Agent | 12 | 4mo ago | MIT | A | 24 | Claude Code |
| fixerasysta-act/agent-flow | Agent | 12 | 4mo ago | MIT | A | 31 | Claude Code |
| deployment-verifierasysta-act/agent-flow | Agent | 12 | 4mo ago | MIT | A | 24 | Claude Code |
| test-engineerasysta-act/agent-flow | Agent | 12 | 4mo ago | MIT | A | 24 | Claude Code |
| backlog-creatorasysta-act/agent-flow | Agent | 12 | 4mo ago | MIT | A | 16 | Claude Code |