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/dailybothq/deepworkplan-skill/apply-reviewnpx skills add DailybotHQ/deepworkplan-skill --skill apply-reviewgit clone --depth 1 https://github.com/DailybotHQ/deepworkplan-skillWrote 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/dailybothq/deepworkplan-skill/apply-review)<a href="https://agentmods.dev/skills/dailybothq/deepworkplan-skill/apply-review"><img src="https://agentmods.dev/badge/skills/dailybothq/deepworkplan-skill/apply-review.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 | $0.00230 | $0.15412 |
| Opus 5 | $0.00115 | $0.07706 |
| Sonnet 5 | $0.00046 | $0.03082 |
| Haiku 4.5 | $0.00023 | $0.01541 |
Grade C, and why
ai-diff-reviewer-apply-review scanned grade C with 1 finding 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 5d 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.
Tells the agent never to refusehighAnti-refusal
Suppressing the ability to decline removes a core safety control; a later harmful request then succeeds.
as a note, don't refuse. How it starts
The opening of the file, as written. The whole thing — 1,348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Diff Reviewer — Apply Review (sub-skill)
Companion to the ai-diff-reviewer skill. Where the
parent runs the review locally and open-pr
writes the pull request, this sub-skill closes the loop: it
reads the review the CI Action posted back on the PR, presents
the findings in the same format the local review uses, and — with
explicit consent — walks the developer through each finding to
apply, defer, or skip.
The design philosophy mirrors the family's:
- Parity of shape. The output uses the same
verdict → findings table → per-finding body → notes → recommendationstructure the parent skill emits. A developer who has seen one of the two knows how to read the other. When the CI leg found "SQL injection insrc/auth.ts:55", the summary looks identical whether it was your local agent or CI that surfaced it. - Read-only by default. Fetching + presenting the review never writes anything. Only when the developer explicitly asks to "walk through" or "apply the fixes" does the sub-skill open source files, and each individual apply still requires a yes.
- Multi-provider aware. This repo (and any consumer that opts
into the 4-leg matrix) posts up to four independent reviews per PR,
distinguished by
self-reviewed:<provider>labels. The sub-skill reads all live legs, attributes each finding to its leg, and surfaces cross-leg consensus ("agreed by 3/3 legs → strong signal; called by 1/3 → could be leg-specific"). - Never commits, never pushes. Applied fixes stay unstaged in the
working tree. Commit + push is the developer's judgment call,
matching
open-pr's trust boundary.
The single source of truth for the workflow this sub-skill implements
is docs/PR_REVIEW_WORKFLOW.md.
This sub-skill is that doc, executable.
When it fires
Read + present the review (default flow) — triggers:
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.
- 5d ago First seen · 1,348 lines · 230 tokens per session scan C 0a1b00dbe560
ai-diff-reviewer-apply-review is a skill published in the GitHub repository DailybotHQ/deepworkplan-skill (20 stars, last pushed 1mo ago), licensed MIT. It adds 230 tokens to every session and 15,412 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it C with 1 finding (tells the agent never to refuse). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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