Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ffroliva/gflow-clinpx agentmods add skills/ffroliva/gflow-cli/statusWrote 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/ffroliva/gflow-cli/status)<a href="https://agentmods.dev/skills/ffroliva/gflow-cli/status"><img src="https://agentmods.dev/badge/skills/ffroliva/gflow-cli/status/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/ffroliva/gflow-cli/status"><img src="https://agentmods.dev/badge/skills/ffroliva/gflow-cli/status.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00058 | $0.00841 |
| Opus 5.5 | $0.00023 | $0.00336 |
| Sonnet 5.5 | $0.00012 | $0.00168 |
| Haiku 4.5 | $0.00006 | $0.00084 |
Grade A, and why
status 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
status — Plan State Reader
Three variants for different contexts. All backed by scripts/dev/active_plan.py.
Variants
status [feature] — Full state
The full picture: which plan file is active, its goal, progress (X/N tasks complete), and the next unchecked task block.
Script invocation:
# With a feature name (from $ARGUMENTS or conversation context):
uv run python scripts/dev/active_plan.py --feature <slug>
# Without a feature name (uses root PLAN.md):
uv run python scripts/dev/active_plan.py
Return: the complete script output verbatim.
When to call:
- Starting a session: "where did we leave off?"
- After completing a task: "what comes next?"
- Before adding scope: "does this belong to the current plan?"
next [feature] — Next task only
The single next unchecked task block. No header noise.
Script invocation: same as status.
Return: only the content from --- Next task --- onward. Drop the Plan / Title /
Goal / Progress header lines entirely.
If no --- Next task --- separator is present (root PLAN.md mode), return the full
script output — the phase block is already task-level content.
If the script output contains "All steps complete", say so and suggest:
/gflow:changelogto review unreleased changes/gflow:releaseif the phase is fully done
When to call:
- "What do I do right now?" — between tasks, no orientation needed
- Resuming mid-session after a context switch
active — Plan identity only
Just which plan is active and its goal. No task detail.
Script invocation: same as status.
Return: only the header lines — Plan path, Title, Goal, Progress count.
- If the output contains
--- Next task ---, stop before that separator. - If no separator is present (root PLAN.md mode —
_summarise_root_plan()output), return the full output; it contains only orientation-level content with no task block.
When to call:
- Before
/gflow:predictor/gflow:scenario: confirm the proposal fits the active scope - Quick check: "are we in a superpowers plan or the root PLAN.md?"
- When context is long and you need a one-line anchor without task block noise
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 · 100 lines · 58 tokens per session scan A 2c7bba748d15
status is a skill published in the GitHub repository ffroliva/gflow-cli (309 stars, last pushed today), licensed MIT. It adds 58 tokens to every session and 841 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
create-issue
Draft and submit a GitHub issue from a user idea or bug description, with bilingual body and correct labels.
issue-creator
A guide for turning unclear user feedback from text, chat excerpts, or screenshots into a focused GitHub issue, a tracked software-work item. It preserves the original request and separates evidence from information that is still unknown.
write-workflows
Author Mendix workflows in MDL — user tasks, decisions, parallel splits, jumps, waits and boundary events, with CREATE, ALTER and DROP. Use when building a business process with human steps, timers or parallel branches.
organize-project
Organise documents into folders and move them between folders and modules with MOVE. Use when a module has grown unstructured, when restructuring a project, or when a document is in the wrong place.
issue-triage
Cluster and classify the mendixlabs/mxcli issues mirrored into the ako/mxcli fork (urgent / fix / proposal / discussion) and keep a triage board issue up to date. Run from a scheduled routine. Does not change code.
hatch3r-board-groom
Ongoing backlog refinement for existing board items. Re-prioritize, reclassify, re-scope, archive stale items, decompose oversized issues, merge duplicates, refresh dependencies, and remediate board health findings.