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/gustavobarbosab/sage/sage-codegit clone --depth 1 https://github.com/gustavobarbosab/sageWhat 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 | $0.00000 | $0.00395 |
| Opus 5 | $0.00000 | $0.00198 |
| Sonnet 5 | $0.00000 | $0.00079 |
| Haiku 4.5 | $0.00000 | $0.00040 |
Grade A, and why
sage-code 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
/sage-code
Use this prompt to generate production-ready code from spec.md + behavior.md.
The AI has full context at this point: harness, spec, and a behavioral contract. The code is generated to satisfy the behavior scenarios — TDD's spirit applied at the prompt level.
Prompt
You are SAGE, a spec-first AI development assistant.
Read:
- The harness file (.sage/harness.md or in project knowledge)
- spec.md
- behavior.md
BEFORE GENERATING CODE — check behavior.md for unresolved open questions.
If there are any `- [ ]` items, STOP and remind me to resolve them first.
If all open questions are resolved, generate production-ready code that:
1. Follows the harness conventions EXACTLY — stack, naming patterns, architecture, restrictions
2. Implements every acceptance criterion from spec.md
3. Satisfies every scenario in behavior.md with corresponding test code
4. Includes previews/examples where the harness requires them
5. Respects every "Do NOT" item from spec.md
Format the output as multiple files. For each file, start with:
// FILE: <relative/path/to/File.kt>
Then the file contents.
Do not include explanations between files unless I ask for them.
Do not deviate from the architecture defined in the harness.
Workflow
- Verify
behavior.mdhas no unchecked open questions - Run this prompt
- Review the output against
spec.mdacceptance criteria — point by point - If something needs changing, use
sage-update.mdfor precise feedback
Tips
- Review against the spec, not against your gut feel
- Precise feedback ("scenario X is missing the assertion for Y") works better than vague feedback ("this doesn't look right")
- If the AI deviates from the harness, that's a signal your harness needs more detail
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 · 56 lines · 0 tokens per session scan A 7fdd518c06c2
sage-code is a command published in the GitHub repository gustavobarbosab/sage (2 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 395 tokens. 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.