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.initgit 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 | $0.00017 | $0.01018 |
| Opus 5 | $0.00009 | $0.00509 |
| Sonnet 5 | $0.00003 | $0.00204 |
| Haiku 4.5 | $0.00002 | $0.00102 |
Grade A, and why
concept:init 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/concept:init
Initialize a new concept development session.
Procedure
Step 1: Create Workspace
Run the init script to create the .concept-dev/ workspace:
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/init_session.py "$(pwd)"
Then initialize the assumption registry:
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/assumption_tracker.py --registry .concept-dev/assumption_registry.json init
If a session already exists, report the existing session details and ask:
- [A] Resume existing session (use
/concept:resume) - [B] Start fresh (archive existing
.concept-dev/to.concept-dev.bak.TIMESTAMP/)
Step 2: Detect Research Tools
Detect available research tools using two methods:
2a: Detect Python packages (via shell import check)
Run check_tools.py to detect Python packages like crawl4ai. This uses python3 -c "import <pkg>" to check if the package is importable, and updates state.json with the results:
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/check_tools.py --state .concept-dev/state.json --json
Python Packages (detected by check_tools.py):
crawl4ai— Deep web crawling (used viaweb_researcher.py, not MCP)
Always Available:
- WebSearch (built-in)
- WebFetch (built-in)
2b: Detect MCP tools (via ToolSearch)
Probe for available MCP research tools by attempting ToolSearch for each tier:
Tier 1 (Free MCP — probe each):
mcp__jina— Document parsingmcp__fetch— MCP fetchmcp__paper_search— Academic papers
Tier 2 (Configurable — probe each):
mcp__tavily— AI searchmcp__semantic_scholar— Academic APImcp__context7— Documentation search
Tier 3 (Premium — probe each):
mcp__exa— Neural searchmcp__perplexity— Perplexity Sonar
For each MCP tool, attempt a ToolSearch with select:<tool_name>. If found, mark as available. Merge MCP results with the Python package results already in state.json:
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 · 142 lines · 17 tokens per session scan A f8801531a6e6
concept:init is a command published in the GitHub repository ddunnock/claude-plugins (12 stars, last pushed 5mo ago), licensed MIT. It adds 17 tokens to every session and 1,018 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.