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 commands/ffroliva/gflow-cli/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 | $0.00022 | $0.00260 |
| Opus 5 | $0.00011 | $0.00130 |
| Sonnet 5 | $0.00004 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
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 yesterday.
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.
What it actually says
/gflow:plan <feature> — Create a feature plan
Read skills/plan/SKILL.md and follow its protocol now, passing $ARGUMENTS as the feature description.
Do not call
Skill(skill="plan")— the repo'sskills/*/SKILL.mdfiles are plain Markdown, not registered as Skill-tool-invocable. Read the file directly instead.
The skill at skills/plan/SKILL.md gathers predict/scenario context, asks ≤3
clarifying questions, decomposes the feature into atomic committable tasks with
step + test checklists, and writes docs/superpowers/plans/<date>-<slug>/PLAN.md.
Typical workflow:
/gflow:predict <proposal> → GO / CAUTION / STOP
/gflow:scenario <feature> → edge cases + BDD skeleton
/gflow:plan <feature> → writes PLAN.md ← this command
/gflow:status → surfaces next task
/gflow:check → before each commit
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.
- yesterday First seen · 23 lines · 22 tokens per session scan A 7f53cf0760d5
plan is a command published in the GitHub repository ffroliva/gflow-cli (136 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 260 once invoked, about $0.0001 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 commands, from other repositories
MIGRATE_DESIGN
Design doc for the migration tool PR. Author: Sol ([email protected]). Co-authored-by: wakesync.
diff-script
Compare an MDL script against the project's current state.
toh-help
Display all Toh Framework commands and quick usage guide.
doctor
Run procedural readiness checks for goal-flight.
sdd-setup
Onboarding wizard — DocLanguage, Memory Bank, architecture snapshot, working agreements (quality gate, TDD working mode).
pipeline-undo
Undo a pipeline run's result. With worktree isolation (the current engine), this is clean and low-risk: a run never touches your checkout — its result lives only on a pipeline/ branch (and, for a --push run, on the remote). "Undo" therefore means deleting that branch and its worktree, not reverting your working tree.