Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/bricerising/enterprise-software-playbooknpx agentmods add skills/bricerising/enterprise-software-playbook/forecastWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bricerising/enterprise-software-playbook/forecast)<a href="https://agentmods.dev/skills/bricerising/enterprise-software-playbook/forecast"><img src="https://agentmods.dev/badge/skills/bricerising/enterprise-software-playbook/forecast/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bricerising/enterprise-software-playbook/forecast"><img src="https://agentmods.dev/badge/skills/bricerising/enterprise-software-playbook/forecast.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00082 | $0.03846 |
| Opus 5.5 | $0.00033 | $0.01538 |
| Sonnet 5.5 | $0.00016 | $0.00769 |
| Haiku 4.5 | $0.00008 | $0.00385 |
Grade A, and why
forecast 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 1mo 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Forecast (Predictive Intelligence)
Overview
Predict likely next developments using two engines:
- Internal engine (trajectory): analyzes git history and archobs cluster context to predict what the team is likely to build next — where momentum is concentrated, what kinds of changes are happening, and what areas are growing.
- External engine: uses Bayesian scenario projection, exponential decay weighting, entropy-based surprise scoring, CUSUM change-point detection, and HMM lifecycle classification from collected intelligence feeds to predict external shifts.
While intel tells you what happened, forecast tells you what's likely to happen next — internally from development patterns and externally from ecosystem signals.
Success looks like: forward-looking intelligence with ranked scenarios, development momentum analysis, and actionable recommendations that a team can act on before events materialize.
Chooser (When to Use)
| Situation | Mode |
|---|---|
| "What are we likely to build next?" | Internal |
| "Where is development concentrated?" | Internal |
| "What external shifts should we prepare for?" | External |
| "What's going to happen next?" (general) | Combined — cross-references internal velocity with external ecosystem signals |
| "What's the full picture?" | Combined — produces compound insights (e.g., "heavy investment in a sinking dependency") |
| "What's the market doing?" / "Technology landscape" | External |
| "What happened recently?" | intel |
| "How is our codebase structured?" | archobs |
| "Plan the implementation" | plan |
Default to Combined mode unless the user explicitly scopes to internal-only or external-only. Cross-referencing internal development velocity against external ecosystem signals produces compound insights that neither engine generates alone.
Inputs / Outputs
Inputs: Archobs data — cluster velocity, drift, file risks (internal engine); intel data — collected feeds, CUSUM breaks, chain patterns (external engine); mode selection (internal/external/combined).
Outputs: Trajectory predictions (internal), ranked scenarios with confidence levels (external), cross-domain synthesis (combined). Consumed by plan (roadmap), architecture (boundary decisions), spec (versioning guidance).
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 217 lines · 82 tokens per session scan A f3b2496e0756
forecast is a skill published in the GitHub repository bricerising/enterprise-software-playbook (8 stars, last pushed 7d ago), licensed Apache-2.0. It adds 82 tokens to every session and 3,846 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
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