dv-workflow

dv-workflow is a skill for Claude Code from DeepVista-AI/deepvista-cli. It costs 84 tokens per session (2,569 once invoked), scanned B, original, Apache-2.0.

A session workflow tracker that turns a Claude Code session into a saved DeepVista workflow skill. It records the goal, breaks it into steps, and keeps step status and session details synchronized.

In plain words
What is it for?
Capturing a session goal, creating a three-to-seven-step workflow, continuing or finalizing an active workflow, and recording node status, timestamps, errors, and session metrics.
Why use it?
It preserves a repeatable record of how work was done and lets a session continue from its last active step. It asks for confirmation before proceeding with the planned steps.

Skill for Claude Code

Written for Claude Code: PostToolUse hook event. Also seen: reads .claude/ paths; mentions Claude Code.

Good fit Capturing a session goal, creating a three-to-seven-step workflow, continuing or finalizing an active workflow, and recording node status, timestamps, errors, and session metrics.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/deepvista-ai/deepvista-cli/dv-workflow
Install

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.

Any agent
npx skills add DeepVista-AI/deepvista-cli --skill dv-workflow
Clone the repo
git clone --depth 1 https://github.com/DeepVista-AI/deepvista-cli

Made for: Claude Code.

Wrote 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.

agentmods badge for dv-workflow

README.md
[![agentmods](https://agentmods.dev/badge/skills/deepvista-ai/deepvista-cli/dv-workflow/github.svg)](https://agentmods.dev/skills/deepvista-ai/deepvista-cli/dv-workflow)
Your own site
<a href="https://agentmods.dev/skills/deepvista-ai/deepvista-cli/dv-workflow"><img src="https://agentmods.dev/badge/skills/deepvista-ai/deepvista-cli/dv-workflow/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.

agentmods 80×15 button for dv-workflow

Your own site · 80×15
<a href="https://agentmods.dev/skills/deepvista-ai/deepvista-cli/dv-workflow"><img src="https://agentmods.dev/badge/skills/deepvista-ai/deepvista-cli/dv-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,569 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 2 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00084 $0.02569
Opus 5.5 $0.00034 $0.01028
Sonnet 5.5 $0.00017 $0.00514
Haiku 4.5 $0.00008 $0.00257

Measured 1mo ago against content hash 2d9d0a356910, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade B, and why

dv-workflow scanned grade B with 2 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.

Reads agent configuration directoriesmediumAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

Check `~/.claude/settings.json` for a Stop hook containing `current-workflow-session`.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

"command": "python3 -c \"import json,pathlib,subprocess,datetime,sys; p=pathlib.Path.home()/'.config/deepvista/current-workflow-session.json'; d=json.loads(p.read_text()) if p.exists() else None; d and [p.write_text(json
skills/dv-workflow/SKILL.md · 308 lines

How it starts

The opening of the file, as written. The whole thing — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/dv-workflow — Session Workflow Tracker

Turns the current Claude Code session into a tracked workflow skill stored in the user's DeepVista vistabase. The goal is captured as a note, a proper workflow skill is synthesized from it, and node status (including timestamps, error details, and session metrics) is kept in sync automatically via a Stop hook after every turn.


Phase 1 — Initialize

Step 1: Check for an existing session

cat ~/.config/deepvista/current-workflow-session.json 2>/dev/null

If the file exists and contains a skill_id, reuse the existing workflow skill — skip to Phase 2. Ask the user:

  • Continue from the last active node
  • Finalize the current workflow (Phase 3), then start fresh

If no session file exists, continue to Step 2.

Step 2: Capture the goal

If the user typed /dv-workflow <goal>, use that text. Otherwise ask: "What should this session accomplish? (One sentence.)"

Step 3: Plan nodes

Break the goal into 3–7 sequential, discrete nodes. Good names are verb phrases: "Understand the codebase", "Implement the feature", "Write tests", "Open PR". Show the plan and confirm with the user before proceeding.

Step 4: Capture the goal as a note

[!NOTE] This one stays type=note on purpose (DV-1911). The user confirmed the plan in step 3, so it is human-initiated, and skill create-from-note emits a cardType="note" chip for its source card — see _build_create_from_note_instruction in deepvista_cli/commands/skill.py. Machine-generated workflow output uses card create --type artifact instead.

deepvista notes create \
  --title "<goal>" \
  --content "Session goal: <goal>

## Planned nodes
1. <node 1>
2. <node 2>
...
"

Extract the note id as NOTE_ID.

Step 5: Synthesize the workflow skill

deepvista skill create-from-note <NOTE_ID> --yes

Wait for the final NDJSON event and extract the skill card id as SKILL_ID.

Step 6: Write the initial execution state

Read the full file on GitHub · 308 lines

Changes

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.

  1. 1mo ago First seen · 308 lines · 84 tokens per session scan B 2d9d0a356910

Subscribe to this mod's changes

dv-workflow is a skill published in the GitHub repository DeepVista-AI/deepvista-cli (9 stars, last pushed 29d ago), licensed Apache-2.0. It adds 84 tokens to every session and 2,569 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 2 findings (reads agent configuration directories, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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