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/plannpx skills add ffroliva/gflow-cli --skill plangit 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 | $0.00089 | $0.01873 |
| Opus 5 | $0.00044 | $0.00937 |
| Sonnet 5 | $0.00018 | $0.00375 |
| Haiku 4.5 | $0.00009 | $0.00187 |
Grade A, and why
plan 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
plan — Feature Plan Creator
Turns a feature description into a task-by-task implementation plan and writes it
to docs/superpowers/plans/<YYYY-MM-DD>-<feature-slug>/PLAN.md.
Position in the gflow-cli workflow:
/gflow:predict <proposal> → GO / CAUTION / STOP verdict
/gflow:scenario <feature> → edge cases + BDD skeleton
/gflow:plan <feature> → writes the task checklist ← this skill
/gflow:status → surfaces next task during execution
/gflow:check → before each commit
When to invoke
- After
/gflow:predictreturns GO or CAUTION - When a backlog item in
PLAN.mdneeds a concrete task breakdown before starting work - Any feature larger than a single isolated file change
When not to invoke
- Simple bug fixes (< 10 lines, no boundary crossing) — go straight to the fix
- Pure doc changes
- A task already fully specified in a superpowers plan — use
/gflow:statusto find it
Protocol
Phase 1 — Gather inputs from context
From /gflow:predict output in context (do not ask if already present):
- Verdict (GO / CAUTION) and confidence score
- Architectural constraints and module placement
- Security risks and mandatory mitigations
- Devil's Advocate simplifications or sequencing blockers
From /gflow:scenario output in context (do not ask if already present):
- Critical and High scenarios → these become must-cover tests in the task checklist
- BDD
Scenario:blocks → seeds the BDD scaffold task
From the feature description passed to this skill:
- Feature name → derive a slug (lowercase, hyphen-separated, no dates)
- Stated goal (one sentence)
From the repo — run once:
uv run python scripts/dev/active_plan.py
Note the active phase name and its open tasks. Then read PLAN.md § "Phase status" and § "Decision log" directly to verify the proposed feature is within current scope and does not contradict an existing ADR. (The script shows the current task, not a backlog index — use PLAN.md for scope confirmation.)
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 · 216 lines · 89 tokens per session scan A bbe187cb9af4
plan is a skill published in the GitHub repository ffroliva/gflow-cli (136 stars, last pushed 2d ago), licensed MIT. It adds 89 tokens to every session and 1,873 once invoked, about $0.0004 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.
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