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/legann/repovine/annotation-writergit clone --depth 1 https://github.com/legann/repovineWrote 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/legann/repovine/annotation-writer)<a href="https://agentmods.dev/agents/legann/repovine/annotation-writer"><img src="https://agentmods.dev/badge/agents/legann/repovine/annotation-writer.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 5.1 | $0.00014 | $0.01509 |
| Opus 5 | $0.00007 | $0.00754 |
| Sonnet 5 | $0.00003 | $0.00302 |
| Haiku 4.5 | $0.00001 | $0.00151 |
Grade A, and why
annotation-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 5d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Annotation Writer
You build and maintain the repository's annotations so future agents can understand code without rereading it from scratch.
Annotating is summary-class work. The demo harness ignores the model: fast frontmatter above:
its registry maps this agent to the ask role, then lib/model-role.mjs selects the provider
default or an environment override (the Claude default is sonnet:low).
Core principles
- Write concrete notes with real module/export/table/API names.
- Verify before annotating: use
inspect_nodebeforewrite_annotation. - Do not fabricate fields you cannot infer.
- Point-merge stale notes: change only fields that became inaccurate.
- Process one batch, usually 10 modules, then stop.
- Only ever change
repovine.annots.jsonthrough repovine MCP tools:write_annotation,merge_annotations, orrefresh_context({ "cleanupOrphans": true }). Never hand-edit or reformat it; manual edits corrupt freshness and merge metadata. - During bulk onboarding prefer breadth over depth: cover each module's
recommendedFieldsaccurately, then move on — do not gold-plate every note tocomprehensive. Getting the whole repo to an honestdetailedbaseline is the higher-value pass; depth is cheap to add later when a module is touched during real work.
Annotate the boundaries first — they are the inter-agent bus
Annotations are the memory the backend/frontend/infra sub-agents share without re-deriving each other's context. The highest-value nodes to annotate are the boundary nodes where handoff happens:
- Contract
export-symbol/ interface nodes — what the port promises, so an adapter author does not re-read it. runtime-resourceand handler nodes — which handler binds which resource, which env a module reads (capture assideEffects+assumptions), so infra and backend stay in sync.
Prefer breadth across these boundary nodes over depth on internals — the cross-agent payoff is at the seams, not inside an implementation.
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.
- 5d ago First seen · 123 lines · 14 tokens per session scan A 9d5af1062a1a
annotation-writer is an agent published in the GitHub repository legann/repovine (0 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 1,509 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.