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 skills add wagnersza/copilot-plugin-cc --skill copilot-promptinggit clone --depth 1 https://github.com/wagnersza/copilot-plugin-ccWrote 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/wagnersza/copilot-plugin-cc/copilot-prompting)<a href="https://agentmods.dev/skills/wagnersza/copilot-plugin-cc/copilot-prompting"><img src="https://agentmods.dev/badge/skills/wagnersza/copilot-plugin-cc/copilot-prompting.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 | $0.00053 | $0.00849 |
| Opus 5 | $0.00026 | $0.00425 |
| Sonnet 5 | $0.00011 | $0.00170 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
copilot-prompting 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.
This is a copy
83% identical to gpt-5-4-prompting — 33 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.
What it actually says
Copilot Prompting
Use this skill when copilot:copilot-rescue needs to ask Copilot or another model-based workflow for help.
Prompt Copilot like an operator, not a collaborator. Keep prompts compact and block-structured with XML tags. State the task, the output contract, the follow-through defaults, and the small set of extra constraints that matter.
Core rules:
- Prefer one clear task per Copilot run. Split unrelated asks into separate runs.
- Tell Copilot what done looks like. Do not assume it will infer the desired end state.
- Add explicit grounding and verification rules for any task where unsupported guesses would hurt quality.
- Prefer better prompt contracts over raising reasoning or adding long natural-language explanations.
- Use XML tags consistently so the prompt has stable internal structure.
Default prompt recipe:
<task>: the concrete job and the relevant repository or failure context.<structured_output_contract>or<compact_output_contract>: exact shape, ordering, and brevity requirements.<default_follow_through_policy>: what Copilot should do by default instead of asking routine questions.<verification_loop>or<completeness_contract>: required for debugging, implementation, or risky fixes.<grounding_rules>or<citation_rules>: required for review, research, or anything that could drift into unsupported claims.
When to add blocks:
- Coding or debugging: add
completeness_contract,verification_loop, andmissing_context_gating. - Review or adversarial review: add
grounding_rules,structured_output_contract, anddig_deeper_nudge. - Research or recommendation tasks: add
research_modeandcitation_rules. - Write-capable tasks: add
action_safetyso Copilot stays narrow and avoids unrelated refactors.
How to choose prompt shape:
- Use built-in
revieworadversarial-reviewcommands when the job is reviewing local git changes. Those prompts already carry the review contract. - Use
taskwhen the task is diagnosis, planning, research, or implementation and you need to control the prompt more directly. - Use
task --resume-lastfor follow-up instructions on the same Copilot thread. Send only the delta instruction instead of restating the whole prompt unless the direction changed materially.
Working rules:
- Prefer explicit prompt contracts over vague nudges.
- Use stable XML tag names that match the block names from the reference file.
- Do not raise reasoning or complexity first. Tighten the prompt and verification rules before escalating.
- Ask Copilot for brief, outcome-based progress updates only when the task is long-running or tool-heavy.
- Keep claims anchored to observed evidence. If something is a hypothesis, say so.
Prompt assembly checklist:
- Define the exact task and scope in
<task>. - Choose the smallest output contract that still makes the answer easy to use.
- Decide whether Copilot should keep going by default or stop for missing high-risk details.
- Add verification, grounding, and safety tags only where the task needs them.
- Remove redundant instructions before sending the prompt.
Model selection notes:
- GPT-5.4: best for complex multi-step coding, deep diagnosis, and adversarial review.
- GPT-5.3-Codex: preferred for focused implementation, targeted fixes, and code generation.
- Gemini 3.1 Pro: use for research, recommendation, and tasks benefiting from broad context.
Reusable blocks live in references/prompt-blocks.md. Concrete end-to-end templates live in references/copilot-prompt-recipes.md. Common failure modes to avoid live in references/copilot-prompt-antipatterns.md.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 60 lines · 53 tokens per session scan A 24969c72f9d6
copilot-prompting is a skill published in the GitHub repository wagnersza/copilot-plugin-cc (45 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 53 tokens to every session and 849 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to gpt-5-4-prompting, differing in 33 lines, and is treated as a copy.
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