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 skills/corvidlabs/agent-3md/spec-syncnpx skills add CorvidLabs/agent-3md --skill spec-syncgit clone --depth 1 https://github.com/CorvidLabs/agent-3mdWrote 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/skills/corvidlabs/agent-3md/spec-sync)<a href="https://agentmods.dev/skills/corvidlabs/agent-3md/spec-sync"><img src="https://agentmods.dev/badge/skills/corvidlabs/agent-3md/spec-sync.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 | $0.00078 | $0.01049 |
| Opus 5 | $0.00039 | $0.00524 |
| Sonnet 5 | $0.00016 | $0.00210 |
| Haiku 4.5 | $0.00008 | $0.00105 |
Grade A, and why
spec-sync 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spec-Sync Workflow
This project uses spec-sync for bidirectional spec-to-code validation. Specs live in specs/<module>/<module>.spec.md.
Companion files
Each canonical spec may have policy-selected companion files. Read and update the ones present; do not create empty companions only for ceremony:
tasks.md— Work items for this module. Check off tasks (- [x]) as you complete them. Add new tasks if you discover work needed.requirements.md— Acceptance criteria and user stories. These are permanent invariants, not tasks — do not check them off. Update if requirements change.context.md— Architectural decisions, key files, and current status. Update when you make design decisions or change what's in progress.testing.md— Test strategy: automated test locations, manual QA checklists, and edge cases/boundary conditions.design.md(opt-in) — Layout, component hierarchy, design tokens, and asset references. Present whencompanions.designis enabled in config.
Verified SDD change lifecycle (5.0)
For every meaningful source, test, public documentation, schema, or configuration change:
- Run
specsync change new "<intent>" --jsonand conduct the returned interview with the user. - Use
specsync change answer <id> <question-id> <answer> --jsonuntil no questions remain. - Complete the adaptively selected artifacts and semantic deltas. Requirements use stable
REQ-<module>-<number>IDs, a normative SHALL statement, and acceptance criteria. - Ask the user for the definition approval, then run
specsync change approve <id>. - Run
specsync change start <id>before editing implementation code. - Keep tasks and artifacts current, then run
specsync change verify <id>. - Present verification evidence and ask for closing approval. Only after explicit approval,
run
specsync change accept <id>; archive separately withspecsync change archive <id>.
Never invent or self-grant either human approval. If an approved definition changes, its digest
becomes stale and must be approved again. specsync check validates canonical specs plus approved
active deltas, requirement-to-test evidence, change coverage, and CI gates.
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 · 76 lines · 78 tokens per session scan A da4dc22433b7
spec-sync is a skill published in the GitHub repository CorvidLabs/agent-3md (1 stars, last pushed 1mo ago), licensed MIT. It adds 78 tokens to every session and 1,049 once invoked, about $0.0004 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 skills, from other repositories
agent-framework-py-release
Use when cutting a Python release for the microsoft/agent-framework monorepo. Triggers on "bump py versions", "cut a python release", "prepare release PR for python", "release py packages", "bump python to X.Y.Z", or similar requests to bump Python package versions and prepare a release PR. Handles all four lifecycle…
python-package-management
Guide for managing packages in the Agent Framework Python monorepo, including creating new connector packages, versioning, and the lazy-loading pattern. Use this when adding, modifying, or releasing packages.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
build-and-test
How to build and test .NET projects in the Agent Framework repository. Use this when verifying or testing changes.
python-code-quality
Code quality checks, linting, formatting, and type checking commands for the Agent Framework Python codebase. Use this when running checks, fixing lint errors, or troubleshooting CI failures.