pdca-iterator

An agent that repeatedly checks a result and improves it when it does not meet the required quality standard. PDCA means Plan, Do, Check, Act: a cycle of making something, reviewing it, and correcting it.

In plain words
What is it for?
Use it for automatic improvement after a gap check finds problems, until the quality target is reached or the allowed attempts run out.
Why use it?
It removes the need to manually repeat the same review-and-fix cycle when the first result is incomplete.

Agent

Install

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.

agentmods
npx agentmods add agents/ww-w-ai/bkit-claude-code/pdca-iterator
Clone the repo
git clone --depth 1 https://github.com/ww-w-ai/bkit-claude-code
Per session 88 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,950 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00088 $0.02950
Opus 5 $0.00044 $0.01475
Sonnet 5 $0.00018 $0.00590
Haiku 4.5 $0.00009 $0.00295

Measured 2d ago against content hash ddb2d6fe69f1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 2d 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.

agents/pdca-iterator.md · 421 lines

How it starts

The opening of the file, as written. The whole thing — 421 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; re-run gap-detector after each fix cycle; stop when Match Rate >= 90% or max iterations reached; report to report-generator when complete (see Auto-Invoke Conditions and Iteration Control below).

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%

2. Code Quality Evaluator

Uses code-analyzer agent to evaluate code quality.

Evaluation Criteria:
- No critical security issues
- Complexity per function <= 15
- No duplicate code blocks (> 10 lines)
- Test coverage >= 80% (if tests exist)

Read the full file on GitHub · 421 lines

Changes

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.

  1. 2d ago First seen · 421 lines · 88 tokens per session scan A ddb2d6fe69f1

Subscribe to this mod's changes

pdca-iterator is an agent published in the GitHub repository ww-w-ai/bkit-claude-code (594 stars, last pushed 16d ago), licensed Apache-2.0. It adds 88 tokens to every session and 2,950 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 2026-08-30.

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