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 GreatMark/fable-field-guide-skills --skill pitch-explainergit clone --depth 1 https://github.com/GreatMark/fable-field-guide-skillsWrote 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/greatmark/fable-field-guide-skills/pitch-explainer)<a href="https://agentmods.dev/skills/greatmark/fable-field-guide-skills/pitch-explainer"><img src="https://agentmods.dev/badge/skills/greatmark/fable-field-guide-skills/pitch-explainer/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/greatmark/fable-field-guide-skills/pitch-explainer"><img src="https://agentmods.dev/badge/skills/greatmark/fable-field-guide-skills/pitch-explainer.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 | $0.00164 | $0.01402 |
| Opus 5 | $0.00082 | $0.00701 |
| Sonnet 5 | $0.00033 | $0.00280 |
| Haiku 4.5 | $0.00016 | $0.00140 |
Grade A, and why
pitch-explainer 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 9d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pitch & Explainer
Shipping needs buy-in. Reviewers start with the same unknowns the author started with; experts want to see that the failure points they'd probe were accounted for. Write for those unknowns, not the author's pride — the doc succeeds when the reviewer's first question is already answered in it.
Workflow
- Collect the artifacts. Look for
implementation-notes.md(project root) and the chosen prototype under./design-directions/— sibling skills leave them there. Also gather: the plan or spec, the diff, demo media (GIF/screenshots). If notes are missing, mine the session history andgit log -pfor decisions and surprises instead. Ask once if a key artifact is missing, then work with what exists. If a prototype was chosen from multiple directions, say so in the doc — "reviewed 4 directions, ops picked this one" is itself evidence of diligence. - Lead with the demo. The first thing a reviewer sees should be the
thing working — a demo GIF, screenshot, or live link. If no demo media
exists (the common case for agent sessions), embed the work's actual
output — a rendered report, a before/after diff of real behavior, a
terminal transcript of the thing running. For pure backend work with
nothing visual, lead with the single most convincing piece of evidence
that it works: a worked input→output example, a benchmark against the
obvious baseline, or a real request/response pair; a bare passing-test
transcript is the last resort — if used, show named test cases
(
pytest -v), not a row of dots. Never skip this slot; never fake media — a static recreation or mockup is fine only when labeled as such, never presented as the shipped thing. Embed images as base64 data URIs so the file survives being dragged into Slack; downscale screenshots to ~1200px wide and re-encode (JPEG/WebP, quality ~80), keeping the whole file under ~2 MB — if media pushes past that, host it or split it out. In markdown venues that stripdata:URIs (GitHub issues/PRs), attach or link images instead of embedding. - Write one self-contained document (
pitch-<topic>.htmlin the project root; inline CSS/JS, no network; markdown if the venue demands it). Write it in the reviewer's language — default to the language of the conversation; if the reviewer's language differs or is unknown, ask once. For Chinese reviewers use these slot titles: 演示 / 为什么做 / 改变了什么 / 做了什么 / 您可能想问 / 边界与开放问题 / 请您拍板. Do not commit it unless asked; in a git repo, exclude it:n=pitch-<topic>.html; p="$(git rev-parse --git-path info/exclude 2>/dev/null)" && { mkdir -p "$(dirname "$p")" && { grep -qxF "$n" "$p" || echo "$n" >> "$p"; }; } 2>/dev/null || echo "note: could not exclude — leaving it untracked, never staging it"Structure:- The demo — show it working, first.
- The problem — why this exists, in the reviewer's terms.
- What it changes — the before/after in numbers the reviewer cares about (time saved, errors prevented, money unblocked). One or two, honest.
- What was built — the shape of the solution, key decisions and why.
- Anticipated questions — the failure points and edge cases an expert reviewer would probe, each answered honestly. Pull from the implementation notes' Deviations and Surprises if they exist.
- Limitations & open questions — what this deliberately does not do.
- The ask — end with the exact decision requested: approve to ship, or a specific next step with a date.
- Make it droppable, then self-check. One file the user can drop into Slack or an issue, readable on a phone — the deliverable is the file itself; do not render it as a Cursor canvas. Before handing it over, verify: it reads in five minutes — roughly 800 words (or ~1500 Chinese characters), count and cut; every link is a URL the reviewer can actually reach (issue, repo, doc link — never relative file paths; if none exists, inline a short appendix instead); The ask names one exact decision.
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.
- 9d ago First seen · 94 lines · 164 tokens per session scan A d365dbd5f1d2
pitch-explainer is a skill published in the GitHub repository GreatMark/fable-field-guide-skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 164 tokens to every session and 1,402 once invoked, about $0.0008 per session on Opus 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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