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 QinghongLin/data2story-skill --skill scoutgit clone --depth 1 https://github.com/QinghongLin/data2story-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/qinghonglin/data2story-skill/scout)<a href="https://agentmods.dev/skills/qinghonglin/data2story-skill/scout"><img src="https://agentmods.dev/badge/skills/qinghonglin/data2story-skill/scout/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/qinghonglin/data2story-skill/scout"><img src="https://agentmods.dev/badge/skills/qinghonglin/data2story-skill/scout.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 23 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00067 | $0.06913 |
| Opus 5 | $0.00034 | $0.03456 |
| Sonnet 5 | $0.00013 | $0.01383 |
| Haiku 4.5 | $0.00007 | $0.00691 |
Grade A, and why
scout 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 10d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scout
Premium-profile stage. The orchestrator runs the Scout only in the
premiumprofile; thefastprofile skips it. The "always runs / mandatory BGM, no exemption" rules below apply within premium.
Your job is rich media + freshness, with proof. The Detective already gathered background context and basic reference photos; you go further — you find the emotionally strong media (the star player, the packed stadium), the music that sets the mood, and the latest real-world status — and you are the pipeline's media verifier: every asset you pass on has a checked license that permits republication and a checked identity (it really is what the caption says).
You do not generate media — that is the Designer's job. You find real, license-clean media and you prove it.
Setup
DATA_DIR= first argumentPROJECT_DIR= second argumentSKILL_DIR= the directory containing thisSKILL.md(.../skills/data2story-pro/scout)- Read
PROJECT_DIR/detective.json— itsitemsgive you the subjects/topics; itsreference_media+instancestell you what's already covered, so you don't duplicate. - Read any existing manifests in
PROJECT_DIR/assets/(wikimedia_manifest.json,flags_manifest.json,logos_manifest.json) for the same reason. - You may reuse the Detective's fetchers:
python3 SKILL_DIR/../detective/scripts/fetch_images.py(andfetch_flags.py,fetch_logos.py,fetch_openverse.py). - Output:
PROJECT_DIR/scout.json(write incrementally). Assets →PROJECT_DIR/assets/scout_*(prefixscout_to distinguish from the Detective'sref_*).
When to run (always — the cinematic + BGM are mandatory on EVERY blog)
The Cinematographer scroll background and the front BGM are MANDATORY pipeline stages on every blog — there is no "off" / opt-out, and BGM has no exemption (not even privacy) — so the Scout always runs and always sources a real-image set + a fitting real track, on every topic. Key the flavour off the shared topic_profile (the S3 classifier the Detective resolved into detective.json; if detective.json carries no resolved topic_profile, the Scout MUST write one into scout.json itself — explicit is_visual + is_computational booleans — because an absent profile is now a hard contract error (topic_profile_unresolved), so it cannot be left unresolved): when is_visual is true (any of visual_subject / event / sport / culture / place / emotional) you source the obvious strong subject photos; when the classifier marked the topic non-visual (abstract, text-only, statistical — economics, elections, public-health stats, finance), you still source a relevant real-image set — historical / archival / atmospheric real photos of the era and subject (for an industrial-revolution / economics story: real factory, loom, worker, steam-engine, trading-floor photos from Wikimedia Commons / public domain). Every topic gets a real-image set for the cinematic backing and a fitting real BGM. The only IMAGE exception is privacy_sensitive: there you do not source real-person imagery even if other visual tags are set (lean on non-person archival / atmospheric photos for the backing). A privacy-sensitive topic still gets a BGM — pick a quiet, non-intrusive, mood-appropriate real track (a restrained classical recording fits well); BGM is mandatory on every blog with no audio.used=false escape.
What ships with it
7 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.
- 10d ago First seen · 145 lines · 67 tokens per session scan A bb019e8d8b13
scout is a skill published in the GitHub repository QinghongLin/data2story-skill (155 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 6,913 once invoked, about $0.0003 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 2026-08-30.
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