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/predictgit 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 | $0.00062 | $0.00307 |
| Opus 5 | $0.00031 | $0.00153 |
| Sonnet 5 | $0.00012 | $0.00061 |
| Haiku 4.5 | $0.00006 | $0.00031 |
Grade A, and why
predict 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:predict [proposal]
Read skills/predict/SKILL.md and follow its protocol now, passing $ARGUMENTS as the proposal description.
Do not call
Skill(skill="predict")— the repo'sskills/*/SKILL.mdfiles are plain Markdown, not registered as Skill-tool-invocable (only.claude/commands/gflow/*are). Invoking it errors withUnknown skill: predict. Read the file directly instead.
The skill at skills/predict/SKILL.md runs five independent expert personas
(Architect · Security/reCAPTCHA · Performance/Playwright · CLI UX · Devil's Advocate),
resolves conflicts, and returns a GO / CAUTION / STOP verdict with a confidence score.
Typical workflow after a GO or CAUTION:
/gflow:scenario <feature> → edge cases + BDD skeleton
/gflow:plan <feature> → writes PLAN.md task checklist
/gflow:status → surfaces next task during execution
/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 · 26 lines · 62 tokens per session scan A 2d5421c30a16
predict is a command published in the GitHub repository ffroliva/gflow-cli (136 stars, last pushed yesterday), licensed MIT. It adds 62 tokens to every session and 307 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 commands, from other repositories
plan-sprint
Run a design sprint end to end — challenge framing, schedule, exercises, and prototype test plan.
sdd-lifecycle
Spec status, moves between backlog/active/done, plan archiving at completion.
sdd-overview
Workflow overview, current spec status, and the SDD command list.
pm-board
Open the great-pm board — a native, zero-dependency project board (hybrid stage×status Kanban + artifact panels + verdict metrics) served from the great-pm plugin itself. Self-contained, no npm packages.
dev-story
Execute story implementation following a context filled story spec file. Use when the user says "dev this story [story file]" or "implement the next story in the sprint plan".
sprint-planning
Generate sprint status tracking from epics. Use when the user says "run sprint planning" or "generate sprint plan".