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 commands/ddunnock/claude-plugins/concept.problemgit clone --depth 1 https://github.com/ddunnock/claude-pluginsWhat 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 | $0.00027 | $0.01450 |
| Opus 5 | $0.00014 | $0.00725 |
| Sonnet 5 | $0.00005 | $0.00290 |
| Haiku 4.5 | $0.00003 | $0.00145 |
Grade A, and why
concept:problem 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.
How it starts
The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/concept:problem
Phase 2 of concept development: problem definition.
Prerequisites
Run the prerequisite gate check:
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/update_state.py --state .concept-dev/state.json check-gate problem
If this exits non-zero, stop and tell the user to complete the previous phase first.
Then load context:
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/update_state.py --state .concept-dev/state.json show- Read IDEAS.md:
.concept-dev/IDEAS.md
Procedure
Step 1: Set Phase
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/update_state.py --state .concept-dev/state.json set-phase problem
Step 2: Context Loading
Read .concept-dev/IDEAS.md and present a summary:
===================================================================
PROBLEM DEFINITION
===================================================================
Starting from your selected themes:
1. [Theme A] — [brief summary]
2. [Theme B] — [brief summary]
I'll ask questions to refine these into a clear problem statement.
We'll work in batches of 3-4 questions with checkpoints between.
IMPORTANT: If you think of specific solutions or technologies,
I'll note them for Phase 4 (Drill-Down) and keep us focused on
the problem itself.
===================================================================
Step 3: Metered Questioning
Use the problem-analyst agent pattern. Follow the metered questioning approach from references/questioning-heuristics.md.
Questioning Framework (adapted from 5W2H for concept development):
Batch 1: Current State
- What is the current situation? How do things work today?
- Who is affected by this situation? (stakeholders, users, operators)
- What are the consequences of the current state? What's the cost of inaction?
- How long has this been the case?
Checkpoint — Summarize understanding, confirm.
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.
- 2d ago First seen · 213 lines · 27 tokens per session scan A 71402a5a9257
concept:problem is a command published in the GitHub repository ddunnock/claude-plugins (12 stars, last pushed 5mo ago), licensed MIT. It adds 27 tokens to every session and 1,450 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.