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 mir-am/skills-agents-ai-coding --skill work-reportgit clone --depth 1 https://github.com/mir-am/skills-agents-ai-codingWrote 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/mir-am/skills-agents-ai-coding/work-report)<a href="https://agentmods.dev/skills/mir-am/skills-agents-ai-coding/work-report"><img src="https://agentmods.dev/badge/skills/mir-am/skills-agents-ai-coding/work-report/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/mir-am/skills-agents-ai-coding/work-report"><img src="https://agentmods.dev/badge/skills/mir-am/skills-agents-ai-coding/work-report.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 | $0.01815 |
| Opus 5.5 | $0.00010 | $0.00726 |
| Sonnet 5.5 | $0.00005 | $0.00363 |
| Haiku 4.5 | $0.00002 | $0.00181 |
Grade A, and why
work-report 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 22d 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 — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What I do
- Create
.opencode/notes/directory in the project if it doesn't exist - Generate one timestamped markdown report file:
YYYY-MM-DD-HHmm-<description>.md - Explain the user's problem, the proposed solution, and why the change matters
- Add enough project and code context for humans and other AI agents to review the work without digging deeply into the repository
- List changed files visible in git, grouped by created, modified, renamed, and deleted
- Capture a concise step-by-step work log describing what the agent inspected, decided, and changed
- Include per-file patch sections with fenced
diffblocks - Use hybrid patch handling: full patches for small files, truncated patches for large files with a
git diffreference - Record tests or validations that were run, if any
When to use me
Use this skill when:
- User asks for a report of the work completed
- User wants a self-contained handoff artifact for later continuation or review
- Agent finishes meaningful code, docs, or config changes and the result should be documented in one place
Report Workflow
-
Create directory:
mkdir -p .opencode/notes -
Generate filename:
- Extract the main task from conversation context
- Format:
YYYY-MM-DD-HHmm-<brief-description>.md - Example:
2026-04-01-1430-add-work-report.md - Description should be kebab-case, concise, and no more than 4-5 words
- Fallback:
work-report
-
Gather task context:
- Identify the problem the user wanted solved
- Identify the scope of work the agent completed
- Summarize the proposed solution chosen by the agent
- Capture why this change matters to the user or project
-
Gather project context:
- Identify the subsystem, feature area, or workflow touched
- Summarize how the relevant part of the project worked before the change
- Note important constraints, conventions, or related files reviewers should know
- Include only context that helps understand or review the change; avoid dumping unrelated repo background
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.
- 22d ago Changed · -1 lines 02b38e6da4b4
- 1mo ago First seen · 224 lines · 24 tokens per session scan A 29ea202b73aa
work-report is a skill published in the GitHub repository mir-am/skills-agents-ai-coding (2 stars, last pushed 23d ago), licensed MIT. It adds 24 tokens to every session and 1,815 once invoked, about $0.0001 per session on Opus 5.5. 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.
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