Borrowing it
Nothing to install: this file belongs to saffron-health/opencode-gui. 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/saffron-health/opencode-gui/main/.agents/skills/implement-spec/SKILL.mdgit clone --depth 1 https://github.com/saffron-health/opencode-guiWrote 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/saffron-health/opencode-gui/implement-spec)<a href="https://agentmods.dev/skills/saffron-health/opencode-gui/implement-spec"><img src="https://agentmods.dev/badge/skills/saffron-health/opencode-gui/implement-spec/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/saffron-health/opencode-gui/implement-spec"><img src="https://agentmods.dev/badge/skills/saffron-health/opencode-gui/implement-spec.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 | $0.00646 |
| Opus 5.5 | $0.00010 | $0.00258 |
| Sonnet 5.5 | $0.00005 | $0.00129 |
| Haiku 4.5 | $0.00002 | $0.00065 |
Grade A, and why
implement-spec 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 2mo 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.
This is a copy
100% identical to implement-spec — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement the specified phase of the given spec file.
Ultrathink. Follow these steps:
1. Read and understand the spec
Read the entire spec file. Understand the problem overview, solution overview, goals, and non-goals. Pay close attention to the phase you've been asked to implement.
2. Read all important files
Read every file listed in the "Important files/docs/websites for implementation" section of the spec. Also read any external documentation links. Do not start writing code until you thoroughly understand the existing codebase and how your changes fit in.
3. Implement the phase
Work through every task item in the phase, including all success criteria (tests, typechecks, lint checks). A phase is not complete until every task item is done, including running any verification steps.
Run pnpm run typecheck, pnpm test, and pnpm run lint after implementation to catch issues.
4. Verify goals
Check the spec's Goals section. If the phase you just implemented should complete any of those goals, explicitly verify them through integration tests or manual testing of the Electron app using Playwright (see AGENTS.md for Playwright instructions). Do not consider the phase done until achieved goals are verified end-to-end.
5. Mark tasks complete
Mark all completed task items in the spec as done (- [x]) before presenting to the user.
6. Do not commit
When you're done, do not stage or commit your changes. Present the completed work to the user for manual review. The user may request changes.
7. Handle user feedback
If the user asks for changes, implement them. Iterate until the user is satisfied.
8. Update the spec before committing
Once the user approves and asks you to commit:
- Update the spec to reflect what was actually implemented. If the implementation deviated from what was originally specced (e.g. the user asked for a different approach), overwrite the phase content in the spec to match what was done. The spec should always reflect the current state of the codebase, not the original plan.
- Check downstream phases. If your spec changes affect later phases, ask the user whether you should update those phases too.
- Then commit both the implementation and the updated spec together.
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.
- 2mo ago First seen · 57 lines · 24 tokens per session scan A 612522977410
implement-spec is a skill published in the GitHub repository saffron-health/opencode-gui (58 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 646 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to implement-spec, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…