Agent skill

Live Verify

by ffroliva in ffroliva/gflow-cli

Two-part gate for gflow-cli feature/fix work. An agent skill from ffroliva/gflow-cli.

MITAuto-check passedMedia & Creative

Install Live Verify

skills CLI
$ npx skills add ffroliva/gflow-cli --skill live-verify -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ffroliva/gflow-cli live-verify --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ffroliva/gflow-cli.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/live-verify .claude/skills/live-verify && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
live-verify
GitHub stars
264
Token cost
~2.3k tokens
SKILL.md length
1,169 words
Files
1
Skills in repo
17
Repo updated
First seen
Licence
MIT

At a glance

Two-part gate for gflow-cli feature/fix work. An agent skill from ffroliva/gflow-cli.

  • Works in 2 steps: Part 1 — Pre-flight, at the start of… → Part 2 — Live-verify, before claiming…
  • Starting work on a gflow-cli feature
  • SKILL.md covers Part 1 — Pre-flight, Part 2 — Live-verify, Failure-routing… and Driver, plus 2 more sections
  • Calls git, pytest and gh

What it does

Live Verify is an agent skill from ffroliva/gflow-cli. Two-part gate for gflow-cli feature/fix work. Part 1 (Pre-flight): use when starting work on a gflow-cli feature or fix — confirms the checkout reflects current develop before investing effort. Part 2 (Live-verify): use before claiming gflow-cli work done, especially anything touching a generation code path (t2i/i2i/i2v/t2v/r2v) — requires live evidence against real Flow, not just offline tests.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Media & Creative, covering AI video generation. The repository describes itself as: Drive Google Flow from the command line: Veo video and Imagen images, scripted, batched and pipeline-ready. Ships an MCP server so coding agents can drive it too, giving you and… The licence is MIT.

When your agent uses it

  • Starting work on a gflow-cli feature
  • Fix — confirms the checkout reflects current develop before investing effort

Example prompts

  • “/live-verify”

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Part 1 — Pre-flight, at the start of feature/fix work: confirm the checkout reflects
  2. Part 2 — Live-verify, before claiming done (after /code-review and

What it can do on your machine

Read from SKILL.md and the folder at commit cb6d501. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • pytest
    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git and gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Live Verify loads about 2.3k tokens when it runs. Until then it costs about 103 tokens; SKILL.md has 1,169 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~103
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ffroliva/gflow-cli at commit cb6d501, republished under its MIT licence (© ffroliva). 1,169 words, ~2,335 tokens.

Download SKILL.mdSave it as .claude/skills/live-verify/SKILL.md (or your agent's skills folder).
name
live-verify
description
Two-part gate for gflow-cli feature/fix work. Part 1 (Pre-flight): use when starting work on a gflow-cli feature or fix — confirms the checkout reflects current develop before investing effort. Part 2 (Live-verify): use before claiming gflow-cli work done, especially anything touching a generation code path (t2i/i2i/i2v/t2v/r2v) — requires live evidence against real Flow, not just offline tests.
version
1.0

/gflow:live-verify — Live-verification enforcement

gflow-cli reverse-engineers a blackbox: Google Flow. Offline checks (ruff, pyright, unit/BDD tests) verify gflow-cli's own code does what it's supposed to; they cannot verify Flow still behaves the way it was captured, because Flow is external and changes without notice (see #174). This gate enforces two things, both evidence-based (no claim without a fresh verification artifact — see the superpowers:verification-before-completion skill):

  1. Part 1 — Pre-flight, at the start of feature/fix work: confirm the checkout reflects current develop before investing effort.
  2. Part 2 — Live-verify, before claiming done (after /code-review and /ponytail:ponytail-review, before commit/PR): exercise the change against real Flow.

Full design rationale: docs/superpowers/specs/2026-07-19-live-verify-design.md.


Part 1 — Pre-flight

Run before writing any code for a new feature/fix:

bash
git fetch origin
git rev-parse --abbrev-ref HEAD
git rev-list --count HEAD..origin/develop
git log --oneline -5

git rev-list --count HEAD..origin/develop is an asymmetric DAG set-difference — it counts commits develop has that the current HEAD lacks, regardless of HEAD's own private unmerged history. This is what catches a genuinely diverged stale branch, not just a behind-by-fast-forward one.

  • If the count is nonzero: stop. git pull (on develop) or rebase/merge (on a feature branch) before continuing.
  • If the branch's last real commit looks old relative to recent develop activity (a smell for stale WIP — e.g. missing a function/guard that develop already has): stop and surface it. Don't silently proceed, don't silently switch — name the divergence and ask the user how to proceed.
  • A separate sibling checkout is not itself a red flag — this project's workflow routinely uses sibling checkouts for isolated feature branches, and a real feature branch is supposed to differ from develop. The actual signal is "differs from develop in a way that suggests staleness" (missing something develop has), not "differs by adding new work on top of it." When in doubt, diff the specific file(s) about to be touched against origin/develop's version before assuming they match:
bash
git diff origin/develop -- <path/to/file>

Part 2 — Live-verify

"Live" means: drive the real generation commands (t2i, i2i, i2v, and siblings like t2v/r2v where applicable) against a real authenticated Flow account, covering multiple variations of the change — not one happy-path call. A change touching a generation code path is default-in-scope; skipping this gate requires a named reason, not silence.

1. Define the live matrix. Before running anything, name explicitly:

  • Which command(s) does the change touch?
  • Which variations actually exercise it? (E.g. for a mention-resolution fix: a character mention, a media mention, an ambiguous-name case, an unresolvable name.)
  • Which surface(s)? A capability that ships as both a CLI command and an MCP tool has two live paths, not one — and the MCP queued path runs different code (mcp/tools.py → queue payload → worker/codec.py → request → daemon owns download and recording). A CLI run does not exercise it. Decide per change whether the MCP path needs its own live run, and if you skip it, say so with a reason. The cheap version is usually enough: one queued MCP call on a credit-free operation proves the payload keys round-trip, which is the hop that silently no-ops (#495). Offline parity tests cannot see this — they never build a real payload and decode it.

2. Check the cost tier for each variation — the tier follows the operation, not the command family:

  • Bare entity CRUD with no image generation (create_entity, list_characters, patch_entity at the API level; t2i/i2i themselves) are credit-free — run as needed to cover the matrix without a separate confirm each time. Still mind WAF/volume discipline: don't fire dozens of live calls back-to-back without surfacing it to the user first.
  • Anything that generates real media is costed, even on an otherwise-free command family — e.g. gflow character create --face-prompt generates real face/body images and drives real image generation despite being "character CRUD" in name (zero credits, daily-capped); i2v and other video-generation paths are always costed. Always get explicit operator go-ahead before running a costed variation. Batch the ask: name what will run and why, once, not one confirm per call.

3. Run each variation, capture evidence per run. Use the release skill's actual 5-layer ledger shape (skills/release/SKILL.md §4b), adapted per-field to what the change under test produces:

LayerEvidence
File countNew file(s)/DB row(s) produced by the run
Magic bytes / field valueThe specific artifact or field the change is supposed to affect (e.g. a real image's magic bytes, or de-tagged prompt text in the catalog)
Dimensions/shapeFor media output: actual dimensions match the requested aspect ratio/model
Structlog invariantsThe expected log event fired (e.g. mention_resolved, not mention_unresolved)
User-confirmable artifactReal output a human could open and check (image/video file, size > 1024 bytes)

Write this to a lightweight per-feature evidence note at tmp/live-verify/<feature-slug>.md (gitignored — this is not the full docs/LIVE_VERIFICATION_vX.Y.Z.md release ceremony). Fold it into the real LIVE_VERIFICATION doc when the feature ships in a release.

4. On pass: all matrix variations green — proceed to commit.

5. On fail: go to Failure-routing below.

Show full SKILL.md (382 more words)Show less

Failure-routing (reproducibility re-test)

1. Check for a known match first (costed failures only, to avoid an unnecessary re-spend): grep KNOWN_ISSUES.md and open GitHub issues for a matching error signature.

bash
gh issue list --repo ffroliva/gflow-cli --search "<error text>" --state all

2. If no match, re-test once. Free for t2i/i2i — just re-run. For a costed failure, re-testing needs the same operator confirm as any costed run.

3. Compare outcomes and route:

OutcomeRoute
Same failure, same code, no known-issue matchReal bug. Back to execution — fix it (use superpowers:systematic-debugging if the cause isn't obvious). Re-run this gate after the fix.
Different outcome, same code, no changes in between — OR a known-issue matchExternal flake. Do not loop trying to "fix" it. Record it in the evidence note (what failed, that it's not reproducible against unchanged code, link to the matching issue if any). Gate passes-with-caveat for this run.
The failure reveals the plan's premise was wrong (not a bug, not a flake)Back to planning/design, not execution. Don't keep patching code against a wrong premise.

4. Record every outcome in the evidence note — passes, fails, and flakes are all evidence, not just the final green state.

Driver

Main context or superpowers:subagent-driven-development — never a stateless one-shot subagent. Diagnosing a live failure needs memory of what's already been tried; a fresh, context-less subagent call breaks a spike-then-fix-then-retest loop.

Pipeline Continuation (Next Step Handoff)

Upon completing Live Verification:

  1. Verification 🟢 PASSED: Proactively announce: "Live verification passed. Next step: Phase 9 PR Creation & Issue Resolution (/gflow:issue-resolve <N>)."
  2. Verification 🔴 FAILED: Return to Phase 6 Task Execution to resolve the identified transport failure.

Notes

  • This gate does not replace /gflow:check (offline gates before commit), nor pytest -m e2e (the e2e suite against a live profile), nor /gflow:doc-review (release-time doc council) — it fills the gap between them.
  • This skill is not the e2e suite, and does not satisfy the e2e-evidence rule. It drives commands by hand and writes a narrative ledger to a gitignored tmp/live-verify/ note; it never runs pytest -m e2e. A PR touching a Flow surface owes BOTH: the e2e test is the re-runnable regression, this ledger is the record of what was observed once, live. See [[e2e-evidence-is-a-contributor-deliverable]] and CONTRIBUTING § Test categories.
  • Testing this skill itself means dry-running it on the next real feature that touches a generation path — there is no synthetic self-test for a live-Flow gate.

© ffroliva, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/live-verify of ffroliva/gflow-cli.

Open the folder on GitHubat commit cb6d501

Compare with similar skills

Live Verify next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Live Verify this skillffroliva/gflow-cli264—~2.3kAutomated safety check: PassMIT
Video Generationbytedance/deer-flow83k4 repos~1.4kAutomated safety check: PassMIT
Video Shotseternityspring/reelbench-skills8682 repos~1.8kAutomated safety check: NotesApache-2.0
Video Cover Imageitwanger/toBeBetterJavaer18k—~3.3kAutomated safety check: PassNone
Seedancesongguoxs/seedance-prompt-skill2.9k1 repos~2.5kAutomated safety check: PassNone
HyperFrames Video Entry Pointheygen-com/hyperframes59k3 repos~5.2kAutomated safety check: PassApache-2.0

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Questions about Live Verify

What does Live Verify do?

Two-part gate for gflow-cli feature/fix work. An agent skill from ffroliva/gflow-cli. Live Verify is an agent skill from ffroliva/gflow-cli. Two-part gate for gflow-cli feature/fix work.

When should I use Live Verify?

Live Verify fits situations like: starting work on a gflow-cli feature; fix — confirms the checkout reflects current develop before investing effort.

How do I install Live Verify in Claude Code?

Run `npx skills add ffroliva/gflow-cli --skill live-verify -a claude-code`. Or copy the skill folder (skills/live-verify in ffroliva/gflow-cli) into .claude/skills/live-verify in your project. Claude Code loads it when a task matches its description.

How do I install Live Verify in Codex?

Run `npx skills add ffroliva/gflow-cli --skill live-verify -a codex`. Or copy the skill folder (skills/live-verify in ffroliva/gflow-cli) into .agents/skills/live-verify in your project. Codex loads it when a task matches its description.

Can I use Live Verify in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ffroliva/gflow-cli --skill live-verify -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/live-verify, .gemini/skills/live-verify, .github/skills/live-verify and .opencode/skills/live-verify in your project.

What does Live Verify need to run?

Going by SKILL.md and its folder, Live Verify needs the command-line tools its instructions call (git, pytest and gh).

Does Live Verify access the network?

SKILL.md contains no URLs. Its commands use git and gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Live Verify safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Live Verify use?

Live Verify is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Live Verify use?

About 2.3k tokens (SKILL.md is roughly 9.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Live Verify?

Skills that share tags, products or a category with Live Verify: Video Generation (bytedance/deer-flow, 83k stars), Video Shots (eternityspring/reelbench-skills, 868 stars), Video Cover Image (itwanger/toBeBetterJavaer, 18k stars) and Seedance (songguoxs/seedance-prompt-skill, 2.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Live Verify?

ffroliva (a GitHub user) maintains it in ffroliva/gflow-cli, which has 264 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on October 7, 2026.

Source: ffroliva/gflow-cli on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.