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 skills/dioptx/weft/wf-composenpx skills add dioptx/weft --skill wf-composegit clone --depth 1 https://github.com/dioptx/weftWhat 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 | $0.00025 | $0.01495 |
| Opus 5 | $0.00013 | $0.00747 |
| Sonnet 5 | $0.00005 | $0.00299 |
| Haiku 4.5 | $0.00003 | $0.00150 |
Grade A, and why
wf-compose 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compose a Weft Workflow
Read the conversation context, scan available skills, identify gaps, and propose a v2 workflow template with loops and skill blocks.
Arguments
$ARGUMENTS
Modes
| Usage | Behavior |
|---|---|
/wf-compose "review, fix, iterate until clean" |
One-shot: propose from description |
/wf-compose (no args) |
Interactive: ask "What are you trying to accomplish?" |
/wf-compose --from feature-workflow |
Start from existing template, modify based on context |
Step 1: Gather Context
Understand what the user is trying to do:
- Review the recent conversation for intent (what task, what repo, what outcome).
- Check git state:
git branch --show-current 2>/dev/null git diff --stat 2>/dev/null | tail -5 - Check if a weft workflow is already active:
python3 "${CLAUDE_PLUGIN_ROOT}/core/cli.py" status 2>/dev/null - If
--from <template>was provided, load it as the starting point:python3 "${CLAUDE_PLUGIN_ROOT}/core/cli.py" preview <template>
Step 2: Scan Skill Registry
Build a map of what skills are available:
- Read the local skills registry, if any (path varies by setup):
cat "${CLAUDE_SKILLS_REGISTRY:-$HOME/.claude/skills-registry.json}" 2>/dev/null - List weft templates:
python3 "${CLAUDE_PLUGIN_ROOT}/core/cli.py" start - Categorize skills by function (examples — substitute what you have available):
- Review: staff-review, arch-review, code-review, differential-review
- Fix/Polish: fix-polish, refactor, simplify
- Test: infra-test, webapp-testing
- Plan: aot-plan, spec-first
- Research: perplexity, context7, research-loop
- Deploy: deploy-service, pr-ready
Step 3: Gap Analysis
Compare what the user described against available skills:
- Extract skill references from the user's description (explicit names like "/staff-review" or implicit like "review code", "test it", "deploy").
- For each referenced skill, check if it exists in the registry.
- For missing skills, present options:
Missing skill: /devils-advocate Options: 1. Create a stub skill (I'll generate a skeleton) 2. Use /staff-review instead (similar purpose) 3. Skip this step - Wait for user choice on each gap before proceeding.
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 · 167 lines · 25 tokens per session scan A 4b0fe18e93c2
wf-compose is a skill published in the GitHub repository dioptx/weft (22 stars, last pushed 17d ago), licensed MIT. It adds 25 tokens to every session and 1,495 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.
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