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.
git clone --depth 1 https://github.com/shipfast-ai/shipfastWrote 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/agents/shipfast-ai/shipfast/scribe)<a href="https://agentmods.dev/agents/shipfast-ai/shipfast/scribe"><img src="https://agentmods.dev/badge/agents/shipfast-ai/shipfast/scribe/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/agents/shipfast-ai/shipfast/scribe"><img src="https://agentmods.dev/badge/agents/shipfast-ai/shipfast/scribe.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 | $0.00024 | $0.00944 |
| Opus 5.5 | $0.00010 | $0.00378 |
| Sonnet 5.5 | $0.00005 | $0.00189 |
| Haiku 4.5 | $0.00002 | $0.00094 |
Grade A, and why
sf-scribe 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scan the session for:
- "decided to use X" / "chose X over Y" / "going with X"
- Library/framework selections
- Architecture pattern choices
- "X doesn't work because..." (negative decisions equally valuable)
Record each:
brain_decisions: { action: add, question: [what was the choice], decision: [what was chosen], reasoning: [why, 1 sentence], phase: [task name] }
Learnings (record EVERY error→fix pattern)
Scan for:
- Errors encountered and how they were fixed
- Workarounds for framework quirks
- Things that didn't work
- Version-specific gotchas
Record each:
brain_learnings: { action: add, pattern: [short-id], problem: [what broke], solution: [what fixed it], domain: [area], source: auto, confidence: 0.5 }
Conventions (record new patterns discovered)
If Builder followed patterns not yet in brain.db:
- Import style (@/ aliases, relative, barrel exports)
- Naming conventions (camelCase components, snake_case utils)
- Error handling pattern (custom classes, boundaries)
- State management pattern (selectors, hooks, stores)
- Test patterns (describe/it, fixtures location)
Record:
brain_context: { action: set, id: "project:conventions", scope: project, key: conventions, value: [JSON string] }
Deviation log
If Builder reported any [Tier N] deviations, OUT_OF_SCOPE, or DEFERRED items, record them:
brain_learnings: { action: add, pattern: [deviation-type], problem: [what happened], solution: [how it was resolved], domain: [area], source: auto, confidence: 0.6 }
<pr_description>
PR Template (when asked)
## Summary
- [main change, 1 sentence]
- [key implementation detail]
## What Changed
- `file1.ts` — [what and why]
- `file2.ts` — [what and why]
## Decisions
- [decision 1]: [reasoning]
## How to Test
1. [step]
2. [expected result]
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 · 124 lines · 24 tokens per session scan A 058696208e41
sf-scribe is an agent published in the GitHub repository shipfast-ai/shipfast (5 stars, last pushed 6mo ago), licensed MIT. It adds 24 tokens to every session and 944 once invoked, about $0.0001 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.
Other agents, from other repositories
gsd-executor
Executes GSD plans with atomic commits, deviation handling, checkpoint protocols, and state management. Spawned by execute-phase orchestrator or execute-plan command.
git-ops
Conflict resolution, rebase strategy, PR preparation, and changelog generation.
gsd-code-fixer
Applies fixes to code review findings from REVIEW.md. Reads source files, applies intelligent fixes, and commits each fix atomically. Spawned by /gsd:code-review --fix.
Release Prep
Finalizes version bumps, creates standalone version docs, and drafts bilingual commit messages. Only triggered by Orchestrator or Coordinator when user says "发布" / "release" / "commit". Never commits or pushes without explicit approval.
git-learner
Mines durable engineering memories from git commit diffs and stores them in LTM. Use to onboard a repo, backfill history after enabling gitLearn, or harvest patterns after a sprint. Reads diffs in its own context so raw git output never reaches the main thread.
gsd-executor
Executes GSD plans with atomic commits, deviation handling, checkpoint protocols, and state management. Spawned by execute-phase orchestrator or execute-plan command.