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 Agent-Engineer-Master/skill-engineer --skill write-reportgit clone --depth 1 https://github.com/Agent-Engineer-Master/skill-engineerWrote 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/agent-engineer-master/skill-engineer/write-report)<a href="https://agentmods.dev/skills/agent-engineer-master/skill-engineer/write-report"><img src="https://agentmods.dev/badge/skills/agent-engineer-master/skill-engineer/write-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/agent-engineer-master/skill-engineer/write-report"><img src="https://agentmods.dev/badge/skills/agent-engineer-master/skill-engineer/write-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 | $0.00198 | $0.08758 |
| Opus 5.5 | $0.00079 | $0.03503 |
| Sonnet 5.5 | $0.00040 | $0.01752 |
| Haiku 4.5 | $0.00020 | $0.00876 |
Grade C, and why
write-report 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 1mo 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- Built with Agent Engineer Master — get your own production-ready skill: www.agentengineermaster.com/skill-engineer --> How it starts
The opening of the file, as written. The whole thing — 416 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write Report (Analysis Quality Review) — Review-Only Multi-Mode Grader
This is a protocol skill. It grades reader-facing deliverables and ghost-deck storylines. It does NOT edit them. Each invocation runs a single mode and returns a structured response. The calling skill — which has the substantive context that produced the document — writes the spec itself (using a template this skill provides) and applies fixes itself (using references/fix-patterns.md), then re-invokes this skill to verify the fixes landed.
Input convention. Reader-facing deliverables are typically HTML (combined reports with prose + charts/diagrams inline) at standard+ strictness. Markdown is acceptable for working drafts at low strictness. The rubrics and reviewers grade content (argument structure, prose readability) regardless of file format — HTML chrome is ignored, prose and section structure are what's graded. Machine artifacts (YAML datasets, JSON, machine-readable manifests) should NOT be reviewed — they are audit substrate, not reader-facing.
The skill is built on four agents and one mechanism:
specmode (no agent): returns the framework-matched authoring template path. File lookup only.- Pre-flight (review modes only):
artifact-loader(runs once per review audit, buildsload_bearing_index.yaml) spec-judgemode:spec-judgeagent — grades the filled-in authoring spec against the declaredstructural_frameworkreview pass: 1:argument-structure-reviewer— grades document structure against the declaredstructural_frameworkreview pass: 2:readability-reviewer— grades document readability (framework-independent — applies to any prose document)review pass: 3:readability-reviewer(humanization mode) — grades the document against the humanize specs (zero em-dashes, no AI tells, human texture present) after the caller has run thehumanizerskill on it
Humanization is mandatory for reader-facing deliverables — with one scope carve-out. A document that passes structure and readability can still read as machine-written — clean but generic, em-dash-riddled, contrastive-negation tics, no human texture. For any doc_type in {brief, memo, deck, decision-record} at strictness ≥ standard, the end-to-end flow REQUIRES a humanize stage. Scope rule (owner decision, 2026-06-10): Pass 3 applies when the deliverable is human-read prose in the author's voice (memo, letter, post, executive report). It does NOT apply to agent-read, repo-resident documentation — docs whose primary consumer is an agent (or a team's agents) and that live inside a product/code repo with an established house voice (e.g. a product repo's docs/). There, match the repo's existing register instead; within-repo voice consistency beats memo-style consistency. Passes 1–2 still run. For the in-scope case: after Pass 2 clears, the calling skill runs the humanizer skill on the document (the edit), then Pass 3 grades that the humanize specs hold. This matters most for client-facing deliverables produced under the owner's name, where the standing rule is no em-dashes, the owner's voice. write-report still never edits — the humanizer skill performs the edit; write-report Pass 3 verifies it landed. Companion-skill dependency: Pass 3 assumes a humanizer editing skill on the caller's side. Any de-AI editing pass that satisfies references/rubric-humanization.md works; if no humanizer skill is installed, the Phase A priority greps inline in that rubric are the minimum pattern source.
What ships with it
31 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.
- agents/argument-structure-reviewer.md 12 KB
- agents/artifact-loader.md 5.6 KB
- agents/readability-reviewer.md 18 KB
- agents/storyline-judge.md 15 KB
- assets/fixtures/brief-good-structure-bad-prose.md 3.1 KB
- assets/fixtures/brief-no-thesis.md 1.9 KB
- assets/fixtures/brief-with-codes.md 2.4 KB
- evals/evals.json 5.6 KB
- references/applicability-matrix.md 7.2 KB
- references/authoring-templates/adr.md 3.3 KB
- references/authoring-templates/concept-page.md 2.3 KB
- references/authoring-templates/descriptive.md 5.5 KB
- references/authoring-templates/issue-tree.md 5.5 KB
- references/authoring-templates/minto-pyramid.md 7.9 KB
- references/authoring-templates/product-spec.md 2.8 KB
- references/authoring-templates/rumelt-kernel.md 8.3 KB
- references/authoring-templates/scqa-only.md 3.5 KB
- references/calling-contract.md 16 KB
- references/deeper-reading.md 9.4 KB
- references/edge-cases.md 2.8 KB
- references/evidence-substrate.md 3.5 KB
- references/fix-patterns.md 27 KB
- references/framework-selection-guide.md 9.5 KB
- references/learnings.md 2.5 KB
- references/load-bearing-architecture.md 7.6 KB
- references/rubric-humanization.md 5.7 KB
- references/rubric-readability.md 13 KB
- references/rubric-structure.md 11 KB
- references/spec-judge-rubric.md 14 KB
- references/tone-of-voice.md 16 KB
- references/worked-examples.md 18 KB
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.
- 1mo ago First seen · 416 lines · 198 tokens per session scan C dd43df1dee04
write-report is a skill published in the GitHub repository Agent-Engineer-Master/skill-engineer (8 stars, last pushed 3mo ago), licensed MIT. It adds 198 tokens to every session and 8,758 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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A writing guide for turning verified facts and calculations into finished text for a specific audience. It follows the requested language, structure, and length.