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/predictnpx skills add ffroliva/gflow-cli --skill 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 | $0.00078 | $0.02367 |
| Opus 5 | $0.00039 | $0.01184 |
| Sonnet 5 | $0.00016 | $0.00473 |
| Haiku 4.5 | $0.00008 | $0.00237 |
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 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
predict — Pre-Implementation Multi-Persona Analysis
Structured pre-implementation review. Five expert personas assess the proposal independently, then debate, then converge on a verdict with a confidence score. Surfacing architectural, security, performance, and UX flaws before the first commit is the cheapest place to catch them.
When to invoke
Use before implementing any of:
- A new transport strategy (
sapisidhash,cdp_attach,official_veo) - Auth flow changes (new strategy, G12 bypass technique, cookie extraction)
- Selector cascade redesigns affecting
ONBOARDING_SELECTORS,NEW_PROJECT_SELECTORS,FRAME_SLOTS_STRUCT - Schema migrations in
gflow_cli/data/ - New CLI surface or exit-code changes
- Any backlog item with an "investigation gate" in PLAN.md before coding
Skip for: trivial bug fixes (< 10 lines, isolated, no boundary cross), already-approved PLAN.md tasks entering EXECUTE, pure doc changes.
Invocation
/gflow:predict <proposal>
<proposal> is a short description of what you intend to build or change —
one paragraph is enough. Examples:
- "Wire SAPISIDHASH auth header into
_post_jsonfor allaisandbox-paroutes (Issue #15)" - "Add CDP-attach transport as opt-in
--transport cdp_attachalongsideui_automation" - "Redesign
gflow video batchto use a local manifest ledger for skip-already-done" - "Add
AuthBrowserBlockedErrortointernal_chromium.pywhen Google rejects bundled Chromium"
Protocol
Phase 1 — Persona briefings (parallel)
Dispatch five personas simultaneously. Each reads AGENTS.md, PLAN.md, KNOWN_ISSUES.md, and the relevant source files for the proposal. Each assesses independently — no persona sees another's output during Phase 1.
Persona 1 — Architect
Scope: hexagonal target, modular-monolith current shape, dependency direction, module boundary rules.
Asks:
- Does this proposal respect the dependency rule (
interfaces → application → domain ← infrastructure)? - Which module does this live in? Does it fit cleanly or does it need a new module, and if so, is that justified?
- Will this make the eventual DDD graduation harder or easier?
- Are there hidden coupling risks (e.g., a transport leaking into
cli.py, a domain model importing frominfrastructure)? - Does the proposed shape match the existing pattern (Protocol-based ports, frozen dataclasses for value objects,
structlogfor all logging)?
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 · 210 lines · 78 tokens per session scan A ad22c804e7db
predict is a skill published in the GitHub repository ffroliva/gflow-cli (136 stars, last pushed 2d ago), licensed MIT. It adds 78 tokens to every session and 2,367 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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