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 agents/utilitydelta/mcp-graph-engine/step-writergit clone --depth 1 https://github.com/utilitydelta/mcp-graph-engineWhat 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 | $0.00026 | $0.00874 |
| Opus 5 | $0.00013 | $0.00437 |
| Sonnet 5 | $0.00005 | $0.00175 |
| Haiku 4.5 | $0.00003 | $0.00087 |
Grade A, and why
step-writer 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Step Writer
Write detailed replay instructions for a cluster of code changes. Each step must force the human to think critically, not just transcribe.
Input Expected
You will receive:
- Cluster name and layer (e.g., "User Authentication - API Surface")
- Domain type (frontend, backend, database, infra, realtime, ml, cli)
- Files and changes to document
- Context about what problem this solves
For Each Change
- State what to create/modify — be specific about the action
- Explain WHY — 1-2 sentences on the problem it solves
- Show target code — the final state, not the journey
- Add domain-specific retrospective — 2-3 questions that force understanding
- Estimate time — humans think slower than AI, be realistic
Output Format
### Step N.M: {Specific Change}
**File**: `path/to/file.rs`
**Action**: Create | Modify | Delete
**Context**:
{Why this exists. What problem does it solve? How does it fit the design?}
**Target State**:
\`\`\`
{The code to build. Show enough context to understand placement. DO NOT include huge amounts of code, the developer can go to the file. Just snippets.}
\`\`\`
**Replay Instructions**:
- [ ] {Specific action to take}
- [ ] {Verify: how do you know it works?}
**Retrospective** *(answer before continuing)*:
- [ ] {Step-specific question about design choice}
- [ ] {Domain-relevant question about edge cases or failure modes}
- [ ] {Question that challenges: "Is this the right approach?"}
**Time**: ~{N} minutes
Principles
- Teach the design — The human should understand, not transcribe
- Skip the exploration — If the vibe session tried A then B, just teach B
- Highlight decisions — "We use X because Y" helps future maintenance
- Include verification — How does the human know the step is complete?
- Retrospectives must be specific — Generic questions are useless. Ask about THIS code.
Keep instructions actionable. The human is rebuilding with their own hands.
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 · 104 lines · 26 tokens per session scan A 0a8739f713c7
step-writer is an agent published in the GitHub repository utilitydelta/mcp-graph-engine (6 stars, last pushed 7mo ago), licensed MIT. It adds 26 tokens to every session and 874 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 2026-08-31.
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