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 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/report)<a href="https://agentmods.dev/skills/fledgeling-co/fledgeling-plugins/report"><img src="https://agentmods.dev/badge/skills/fledgeling-co/fledgeling-plugins/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/report"><img src="https://agentmods.dev/badge/skills/fledgeling-co/fledgeling-plugins/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 | $0.00533 | $0.08760 |
| Opus 5.5 | $0.00213 | $0.03504 |
| Sonnet 5.5 | $0.00107 | $0.01752 |
| Haiku 4.5 | $0.00053 | $0.00876 |
Grade A, and why
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 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.
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 — 658 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing the session up
One session in, one report out: argued from what the session actually established, designed around its own subject, written to disk as a page you can read and a PDF you can send, and offered in three registers so the same evidence reaches a specialist, a decision-maker and someone meeting the subject for the first time.
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.
The failure this exists to prevent
A session reads forty files, runs a dozen commands, and reaches a conclusion. Asked for a write-up, the obvious move is to narrate the conclusion in confident prose. The result reads well and is unusable, because three very different things render identically:
- "the queue drops 12% of events" — measured, from a command whose output is in the transcript
- "the queue drops 12% of events" — read off one log sample and generalised
- "the queue drops 12% of events" — inferred from two facts that were each established separately
A week later nobody can tell which. Neither can the person you sent it to, and they have less reason to trust you than you do.
So the spine of this skill is: compile the ledger before you design anything. Every claim gets a locator or a label. The citation UI is then generated from the ledger rather than retrofitted onto finished prose, which is the only way the two stay honest with each other.
references/evidence.md carries the research behind every rule below.
Read it when you need to justify or tune a rule, not on every run.
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.
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.
- assets/report-template.html 33 KB
- assets/tldr-template.html 17 KB
- gemini.md 19 KB
- references/design-system.md 7.4 KB
- references/evidence-harvest.md 8.4 KB
- references/evidence.md 12 KB
- references/product-verdicts.md 13 KB
- references/readings.md 15 KB
- references/report-craft.md 32 KB
- references/source-imagery.md 9.5 KB
- references/visualisation.md 17 KB
- scripts/audit_report.py 45 KB runs code
- scripts/export_pdf.mjs 8.7 KB runs code
- scripts/lib/harness.mjs 7.2 KB runs code
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 Changed · +2 lines · +23 tokens per session 1c6e0bf6d490
- 1mo ago First seen · 656 lines · 510 tokens per session scan A d7e8441e3d64
report is a skill published in the GitHub repository fledgeling-co/fledgeling-plugins (2 stars, last pushed 4d ago), licensed MIT. It adds 533 tokens to every session and 8,760 once invoked, about $0.0021 per session on Opus 5.5. 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 2026-09-04.
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