Borrowing it
Nothing to install: this file belongs to aaked-app/aakd. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/aaked-app/aakd/main/.agents/skills/speckit-analyze/SKILL.mdgit clone --depth 1 https://github.com/aaked-app/aakdWrote 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/aaked-app/aakd/speckit-analyze)<a href="https://agentmods.dev/skills/aaked-app/aakd/speckit-analyze"><img src="https://agentmods.dev/badge/skills/aaked-app/aakd/speckit-analyze/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/skills/aaked-app/aakd/speckit-analyze"><img src="https://agentmods.dev/badge/skills/aaked-app/aakd/speckit-analyze.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 | $0.00032 | $0.02557 |
| Opus 5 | $0.00016 | $0.01278 |
| Sonnet 5 | $0.00006 | $0.00511 |
| Haiku 4.5 | $0.00003 | $0.00256 |
Grade A, and why
speckit-analyze 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 8d 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 8d ago First seen · 260 lines · 32 tokens per session scan A a84a54c47017
speckit-analyze is a skill published in the GitHub repository aaked-app/aakd (10 stars, last pushed yesterday), licensed AGPL-3.0. It adds 32 tokens to every session and 2,557 once invoked, about $0.0002 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
review-system-design
Review a system design someone else provided - screenshot, drawio, Mermaid, slides, doc, or IaC - by extracting it into a confirmed fact sheet, then scoring it on the nine axes.
review-ticket
Review a ticket or PR through focused specialist lenses: scope, architecture, security, tests, AC coverage, and PR metadata.
common-feedback-reporter
Pre-write audit for skill violations: checks planned code against loaded skill anti-patterns before any file write. Use when writing Flutter/Dart/TS code or editing SKILL.md files with active project skills. Load as composite; on auto-fixed violation, also load +common/common-learning-log.
code-review
Run an AI-assisted PR code review using multi-layer lenses with confidence scoring.
common-code-review
Conduct high-quality, persona-driven code reviews. Use when reviewing PRs, critiquing code quality, or analyzing changes for team feedback.
factory-rereview
Re-review a pull request after a push — reconcile the previous review against the new commits, look for new defects the push introduced, then a fresh pass over the whole PR, and finish with a verdict on the PR.