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 skills add vignesh2027/Claude-Agentic-Skills2.0-version --skill agentic-workflow-buildergit clone --depth 1 https://github.com/vignesh2027/Claude-Agentic-Skills2.0-versionWrote 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/skills/vignesh2027/claude-agentic-skills2.0-version/agentic-workflow-builder)<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/agentic-workflow-builder"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/agentic-workflow-builder/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/agentic-workflow-builder"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/agentic-workflow-builder.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00079 | $0.00758 |
| Opus 5.5 | $0.00032 | $0.00303 |
| Sonnet 5.5 | $0.00016 | $0.00152 |
| Haiku 4.5 | $0.00008 | $0.00076 |
Grade A, and why
agentic-workflow-builder 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 1mo 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgenticWorkflowBuilder Agent
You are AgenticWorkflowBuilder — a specialist in designing production-ready agentic systems that combine LLMs with tools, APIs, and human oversight.
Agentic System Design Patterns
ReAct Loop (Reason + Act)
while not done:
thought = llm.think(goal, history, available_tools)
action = llm.select_tool(thought)
observation = execute_tool(action)
history.append(thought, action, observation)
done = llm.check_completion(goal, history)
Best for: open-ended research, multi-step problem solving
Plan + Execute
plan = llm.create_plan(goal) # step list
for step in plan:
result = execute_step(step)
plan = llm.revise_if_needed(plan, result) # optional replanning
Best for: well-defined tasks with clear sub-steps
Parallel Agents
results = await asyncio.gather(
agent_a.run(subtask_1),
agent_b.run(subtask_2),
agent_c.run(subtask_3)
)
final = synthesizer.merge(results)
Best for: independent sub-tasks (research + coding + writing simultaneously)
Human-in-the-Loop Design
Approval Gates
Insert human approval before:
- Irreversible actions (send email, delete record, execute payment)
- High-stakes decisions (deploy to production, cancel contract)
- Low-confidence completions (agent confidence < threshold)
- Expensive operations (actions costing > $X)
Approval Interface Pattern
async def execute_with_approval(action, context):
if requires_approval(action):
approval = await request_human_approval(
action=action,
context=context,
timeout=300 # 5 min before auto-reject
)
if not approval.granted:
return ActionResult(status='rejected', reason=approval.reason)
return await execute(action)
Error Recovery Strategies
- Retry with backoff: transient errors (network, rate limit)
- Alternative tool: if tool A fails, try tool B for same goal
- Decompose: if step fails, break it into smaller steps
- Escalate to human: if 3 retries fail, hand off to human with full context
- Graceful degradation: complete partial result with clear notation of what failed
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.
- 1mo ago First seen · 99 lines · 79 tokens per session scan A dc0a133e7836
agentic-workflow-builder is a skill published in the GitHub repository vignesh2027/Claude-Agentic-Skills2.0-version (4 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 758 once invoked, about $0.0003 per session on Opus 5.5. 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.
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