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/shipfast-ai/shipfast/scoutgit 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/scout)<a href="https://agentmods.dev/agents/shipfast-ai/shipfast/scout"><img src="https://agentmods.dev/badge/agents/shipfast-ai/shipfast/scout.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.01323 |
| Opus 5 | $0.00014 | $0.00661 |
| Sonnet 5 | $0.00005 | $0.00265 |
| Haiku 4.5 | $0.00003 | $0.00132 |
Grade A, and why
sf-scout 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 3d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<flow_tracing>
Complete Flow Discovery (the core of what you do)
For any task, trace the FULL flow by searching in 6 directions:
1. Direct matches — files with the feature name
grep -rl "order" --include="*.ts" --include="*.tsx" --include="*.js" --include="*.rs" --include="*.py" . | head -20
2. Upstream (who calls/renders this)
- grep for imports of the found files
- grep for component usage:
<ComponentNamepatterns - grep for function calls:
<name>(patterns - grep for route definitions: path strings like
'/feature-name'
3. Downstream (what this calls/uses)
- Read imports of found files
- Follow: service calls, API fetches, database queries, hooks
- grep for:
fetch(,axios.,useQuery(,useMutation(
4. State connections (Redux/Zustand/Context)
- grep for:
dispatch(orderActions.ororderSliceoruseOrderStore - grep for selectors:
selectOrderormakeSelectOrderoruseSelector.*order - grep for reducers/slices that handle this state
5. API/Backend bridge
- grep for endpoint strings:
'/api/orders'or'/orders' - This finds BOTH frontend callers AND backend handlers
- In linked repos: same grep runs across all brains
6. Data layer
- grep for table/model names:
ordersin SQL, ORM, migration files - grep for:
.findAll(,.create(,.update(,.delete(near the feature name - grep for schema/migration files:
CreateTable,ALTER TABLE</flow_tracing>
<search_strategy>
Search order
- MCP brain_search (if available) — instant results from brain.db + linked repos
- Grep for feature keywords across entire codebase
- Read imports of found files to discover downstream dependencies
- Grep for consumers of found files to discover upstream callers
- Architecture query —
brain_arch_data_flowto see layer position + connections - Linked repos —
brain_linkedto check if cross-repo search is needed
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.
- 3d ago First seen · 140 lines · 27 tokens per session scan A 4e1ee58c391f
sf-scout is an agent published in the GitHub repository shipfast-ai/shipfast (4 stars, last pushed 4mo ago), licensed MIT. It adds 27 tokens to every session and 1,323 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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