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/ffroliva/gflow-cli/statusnpx skills add ffroliva/gflow-cli --skill statusgit clone --depth 1 https://github.com/ffroliva/gflow-cliWhat 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 | $0.00058 | $0.00841 |
| Opus 5 | $0.00029 | $0.00420 |
| Sonnet 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 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 — 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.
- 2d ago First seen · 100 lines · 58 tokens per session scan A 2c7bba748d15
status is a skill published in the GitHub repository ffroliva/gflow-cli (136 stars, last pushed 2d ago), licensed MIT. It adds 58 tokens to every session and 841 once invoked, about $0.0003 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.
Other skills, from other repositories
repomix
Pack and analyze codebases into AI-friendly single files using Repomix. Use when the user wants to explore repositories, analyze code structure, find patterns, check token counts, or prepare codebase context for AI analysis. Supports both local directories and remote GitHub repositories.
agent-carnet
Use this skill when the user asks to save, recall, find, or organize notes. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check the notebook', 'find in carnet'. Also use proactively when discovering findings worth preserving across sessions.
graph-mutation-plan
Cookbook for composing an applygraphmutations plan — stable entitykey patterns, the canonical label/edge vocabulary, evidence/invalidation/confidence discipline, and a worked example. Load this when building a non-trivial mutation plan.
muapi-media-editing
Edit and enhance images and videos with AI via muapi.ai — prompt-based editing, upscaling, background removal, face swap, lipsync, video effects, and more.
plano-agent-orchestration
Improve multi-agent orchestration in Plano. Use for agent registration, agent listener wiring, and capability-focused agent descriptions for accurate routing.
plano-filter-guardrails
Harden Plano filter chains and guardrails. Use for MCP filter setup, prompt guard responses, and safe filter ordering.