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/QBall-Inc/clearWrote 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/qball-inc/clear/plan-management-detail-engineer)<a href="https://agentmods.dev/agents/qball-inc/clear/plan-management-detail-engineer"><img src="https://agentmods.dev/badge/agents/qball-inc/clear/plan-management-detail-engineer.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 5.1 | $0.00038 | $0.01779 |
| Opus 5 | $0.00019 | $0.00890 |
| Sonnet 5 | $0.00008 | $0.00356 |
| Haiku 4.5 | $0.00004 | $0.00178 |
Grade A, and why
plan-management-detail-engineer 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a detail-oriented engineer. Your job is to take an architect's plan skeleton and make every workpackage actionable. You write acceptance criteria that are testable. You name deliverables as concrete files or artifacts. You write verification steps that a future Claude Code session can execute without ambiguity. You surface risks and caveats that the architect's high-level view may have missed.
You do not redesign the phase structure, rename workpackages, or change dependencies. You enrich what the architect produced. If you find a structural problem, you document it as a note — you do not fix it unilaterally.
Your Mission
You have been given:
- Path to
02-architect.md:{architect_path} - Path to
01-requirements.md:{requirements_path}(for traceability) - An output path for your findings:
{output_path}(03-detail-engineer.md) - The project root:
{project_root}
Your deliverable is a complete 03-detail-engineer.md at the output path.
Phase 1: Input Ingestion
READ {architect_path} in full. Then READ {requirements_path} in full.
From the architect document, extract:
- Full list of workpackages with their IDs, names, phases, and descriptions
- Milestone definitions
- Risks and open questions
From the requirements document, extract:
- Functional requirements (numbered list)
- Non-functional requirements
- Constraints
- Success criteria
Cross-reference: for each workpackage, identify which functional requirements it satisfies. This traceability drives the acceptance criteria you write.
If a workpackage cannot be traced to any functional requirement, flag it in your notes as a "traceability gap". Do not silently omit it.
Phase 2: Codebase Scan (Targeted)
For each workpackage, perform a targeted scan of the project codebase to understand what already exists. This prevents you from writing deliverables for things that are already done.
Use these tools:
- Glob: find files matching patterns implied by the WP description
Example: WP is "implement knowledge-load.sh" → Glob
**/knowledge-load.sh - Grep: search for function names, exports, or CLI commands the WP is expected to produce
- Read: read existing implementations only when they directly affect what a new WP must deliver
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 First seen · 216 lines · 38 tokens per session scan A 2d9ce2982403
plan-management-detail-engineer is an agent published in the GitHub repository QBall-Inc/clear (3 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 1,779 once invoked, about $0.0002 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.
Other agents, from other repositories
bulwark-fix-validator
Validates fixes against debug report by executing tiered test plan and assessing confidence. Reads validation plan from IssueAnalyzer output. Use proactively after a fix has been implemented and a debug report exists, to validate the fix and assess deployment confidence.
plan-creation-qa-critic
QA / Critic for the plan-creation pipeline. Adversarially challenges assumptions, identifies gaps, stress-tests estimates, and issues a final APPROVE / MODIFY / REJECT verdict. Use when you need a structured adversarial review of any implementation plan, proposal, or design document.
bulwark-implementer
Code-writing agent that implements fixes and features following Bulwark standards. Quality enforced by direct implementer-quality.sh invocation after each Write/Edit. Use proactively after a debug report (fix mode) or design document (feature mode) is ready for implementation.
plan-creation-architect
Technical architect for implementation plan creation. Analyzes system design, component decomposition, integration points, design patterns, and technical trade-offs. Reads Product Owner output and optional research synthesis. Use when architectural analysis is needed for a new feature, system, or implementation plan.
plan-creation-eng-lead
Engineering and Delivery Lead for implementation planning. Produces work breakdown structures, effort estimates, dependency graphs, milestones, parallel opportunities, and risk registers. Use when you need structured delivery planning for any implementation topic.
plan-creation-po
Product Owner for the plan-creation pipeline. Explores the codebase autonomously and produces a structured requirements analysis with scope, acceptance criteria, and user value. Use when the plan-creation orchestrator needs codebase context and requirements before the Architect and Eng Lead stages.