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/rofid-c/codebase-analyzer-mcp/full-output-enforcementnpx skills add rofid-c/codebase-analyzer-mcp --skill full-output-enforcementgit clone --depth 1 https://github.com/rofid-c/codebase-analyzer-mcpWrote 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/rofid-c/codebase-analyzer-mcp/full-output-enforcement)<a href="https://agentmods.dev/skills/rofid-c/codebase-analyzer-mcp/full-output-enforcement"><img src="https://agentmods.dev/badge/skills/rofid-c/codebase-analyzer-mcp/full-output-enforcement.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 | $0.00044 | $0.00566 |
| Opus 5 | $0.00022 | $0.00283 |
| Sonnet 5 | $0.00009 | $0.00113 |
| Haiku 4.5 | $0.00004 | $0.00057 |
Grade A, and why
full-output-enforcement 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 4d 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 full-output-enforcement — 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Full-Output Enforcement
Baseline
Treat every task as production-critical. A partial output is a broken output. Do not optimize for brevity — optimize for completeness. If the user asks for a full file, deliver the full file. If the user asks for 5 components, deliver 5 components. No exceptions.
Banned Output Patterns
The following patterns are hard failures. Never produce them:
In code blocks: // ..., // rest of code, // implement here, // TODO, /* ... */, // similar to above, // continue pattern, // add more as needed, bare ... standing in for omitted code
In prose: "Let me know if you want me to continue", "I can provide more details if needed", "for brevity", "the rest follows the same pattern", "similarly for the remaining", "and so on" (when replacing actual content), "I'll leave that as an exercise"
Structural shortcuts: Outputting a skeleton when the request was for a full implementation. Showing the first and last section while skipping the middle. Replacing repeated logic with one example and a description. Describing what code should do instead of writing it.
Execution Process
- Scope — Read the full request. Count how many distinct deliverables are expected (files, functions, sections, answers). Lock that number.
- Build — Generate every deliverable completely. No partial drafts, no "you can extend this later."
- Cross-check — Before output, re-read the original request. Compare your deliverable count against the scope count. If anything is missing, add it before responding.
Handling Long Outputs
When a response approaches the token limit:
- Do not compress remaining sections to squeeze them in.
- Do not skip ahead to a conclusion.
- Write at full quality up to a clean breakpoint (end of a function, end of a file, end of a section).
- End with:
[PAUSED — X of Y complete. Send "continue" to resume from: next section name]
On "continue", pick up exactly where you stopped. No recap, no repetition.
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.
- 4d ago First seen · 50 lines · 44 tokens per session scan A e5bf48b1ae56
full-output-enforcement is a skill published in the GitHub repository rofid-c/codebase-analyzer-mcp (0 stars, last pushed 4d ago), licensed MIT. It adds 44 tokens to every session and 566 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to full-output-enforcement, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
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…