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 fledgeling-co/fledgeling-plugins --skill dossier-reportgit clone --depth 1 https://github.com/fledgeling-co/fledgeling-pluginsWrote 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/fledgeling-co/fledgeling-plugins/dossier-report)<a href="https://agentmods.dev/skills/fledgeling-co/fledgeling-plugins/dossier-report"><img src="https://agentmods.dev/badge/skills/fledgeling-co/fledgeling-plugins/dossier-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/fledgeling-co/fledgeling-plugins/dossier-report"><img src="https://agentmods.dev/badge/skills/fledgeling-co/fledgeling-plugins/dossier-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 | $0.00489 | $0.09026 |
| Opus 5 | $0.00244 | $0.04513 |
| Sonnet 5 | $0.00098 | $0.01805 |
| Haiku 4.5 | $0.00049 | $0.00903 |
Grade A, and why
dossier-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 today.
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 — 657 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Publishing a research page
One topic in, one page out: researched by a panel, argued from a claim graph, designed around its own subject, published to its own subdomain, and offered in three registers so the same evidence reaches a specialist, a decision-maker and a reader meeting the subject for the first time.
The failure this exists to prevent is specific and has happened twice
already in this repo. A panel of five backends ran, research_synthesise
reported "5 never opened", and the page was written from the merged
distillation. The distillation is a coverage difference between reports,
not a summary of them. A page built on it describes what the backends did
not share rather than what they found.
So the spine of this skill is: spend on research, then actually read it, then let the reading decide the page. Everything else follows.
references/evidence.md carries the research this skill is built on —
225 sources across five backends — and every rule below traces to a row
in it. Read it when you need to justify or tune a rule, not on every run.
Using Gemini? Read gemini.md for this skill's task-specific calibration and artifact checks. Its older runs do not establish Gemini 3.8 capability limits: preserve the user's selected implementation lane, and apply a model-specific route only when the current model or an observed failure supports it.
Resolve dependencies in the current runtime. Use the exact installed identifiers for the named plugin skills. dataviz is a standalone skill identifier only when the runtime lists it; if unavailable, use this skill's references/visualisation.md and an available charting or diagram tool, and name the substitution. Discover media and Mobbin tool names from the tool catalog. Reuse a loaded skill's guidance and the same acceptance evidence across phases; routing every decision through its criteria does not require invoking the skill again for each word or figure.
The shape of a run
Ten phases. Phases 1 and 5 both use /trawl:trawl for divergence; phases 0,
4 and 9 are the human checkpoints.
What ships with it
14 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.
- gemini.md 21 KB
- references/aesthetic-direction.md 6.9 KB
- references/evidence.md 25 KB
- references/opus-5-prompting.md 5.6 KB
- references/page-craft.md 37 KB
- references/product-verdicts.md 12 KB
- references/readings.md 13 KB
- references/research.md 5.7 KB
- references/source-imagery.md 8.8 KB
- references/visualisation.md 16 KB
- scripts/audit_page.py 45 KB runs code
- scripts/build_chrome.py 8.3 KB runs code
- scripts/dossier-mark.svg 6.4 KB
- scripts/margin-mark.svg 2.7 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.
- today Changed · +2 lines · +4 tokens per session 4e0bc78fbb50
- 8d ago First seen · 655 lines · 485 tokens per session scan A 17716a346555
dossier-report is a skill published in the GitHub repository fledgeling-co/fledgeling-plugins (2 stars, last pushed yesterday), licensed MIT. It adds 489 tokens to every session and 9,026 once invoked, about $0.0024 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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