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/kesteva/soloflow/prunegit clone --depth 1 https://github.com/kesteva/soloflowWhat 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 | $0.00022 | $0.02000 |
| Opus 5 | $0.00011 | $0.01000 |
| Sonnet 5 | $0.00004 | $0.00400 |
| Haiku 4.5 | $0.00002 | $0.00200 |
Grade A, and why
prune 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/soloflow:prune
Performs a comprehensive pruning pass across the codebase and all CLAUDE.md files. Spins up parallel analysis agents, presents findings for approval, applies approved changes, and runs the test suite to confirm no regressions.
Scope: $ARGUMENTS (optional — limit analysis to a specific directory; defaults to entire project)
Model resolution
Run once:
node "${CLAUDE_PLUGIN_ROOT}/scripts/config/resolve.js" \
--key models.codebase_pruner --key models.claudemd_pruner \
--fallback opus --fallback opus
Line 1 is the codebase-pruner model; line 2 is the claudemd-pruner model.
Step 0: Check initialization
If .soloflow/ does not exist, report: "SoloFlow not initialized. Run /soloflow:init first." and stop.
Step 1: Create prune branch
- Verify working tree is clean:
git status --porcelain. If dirty, tell the user to commit or stash first and stop. - Capture current branch:
git rev-parse --abbrev-ref HEAD. - Create and checkout a new branch:
git checkout -b soloflow/prune-$(date +%Y%m%d-%H%M%S). - Print:
Prune branch: {branch_name} (base: {base_branch}).
Step 2: Parallel analysis
Spawn two agents in parallel using the Agent tool:
Agent A: Codebase Pruner
- Agent definition:
codebase-pruner - Task: "Audit the codebase at
{project_root}for dead code, redundancy, inefficiency, and orphaned assets. {If $ARGUMENTS specifies a scope, add: Focus on{scope}.} Produce a structured pruning report." - Model: resolved
models.codebase_pruner(see Model resolution above)
Agent B: CLAUDE.md Pruner
- Agent definition:
claudemd-pruner - Task: "Audit all CLAUDE.md files in
{project_root}for redundancy, staleness, scope misplacement, and content that should move to specialized reference files. Produce a structured pruning report." - Model: resolved
models.claudemd_pruner(see Model resolution above)
Wait for both agents to complete.
Step 3: Present findings for approval
Combine the two reports into a unified presentation. For each category, present items grouped by type:
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 · 174 lines · 22 tokens per session scan A 8949f4e14d55
prune is a command published in the GitHub repository kesteva/soloflow (40 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 2,000 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.