report

report is a skill for Claude Code from fledgeling-co/fledgeling-plugins. It costs 533 tokens per session (8,760 once invoked), scanned A, original, MIT.

A report-writing skill that turns the findings of a Claude session into a self-contained web page, an A4 PDF, and a one-page summary. It offers beginner-friendly, decision-focused, and technical versions of the same evidence.

In plain words
What is it for?
Use it to document research, product analysis, or technical investigations in cited reports that can be viewed online or sent as a PDF.
Why use it?
Important conclusions can be hard to review when they are buried in session notes. This makes the reasoning, claims, sources, figures, and limitations easier to read and share.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents; names the AskUserQuestion tool; mentions Gemini CLI.

Part of the report plugin — 1 skill shipped together

Good fit Use it to document research, product analysis, or technical investigations in cited reports that can be viewed online or sent as a PDF.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/fledgeling-co/fledgeling-plugins/report
Install

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.

Any agent
npx skills add fledgeling-co/fledgeling-plugins --skill report
Clone the repo
git clone --depth 1 https://github.com/fledgeling-co/fledgeling-plugins

Made for: Claude Code.

Or install report, the plugin that ships this one along with the rest of its 1 skill.

Wrote 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.

agentmods badge for report

README.md
[![agentmods](https://agentmods.dev/badge/skills/fledgeling-co/fledgeling-plugins/report/github.svg)](https://agentmods.dev/skills/fledgeling-co/fledgeling-plugins/report)
Your own site
<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.

agentmods 80×15 button for report

Your own site · 80×15
<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>
Per session 533 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,760 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 1mo ago against content hash 1c6e0bf6d490, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/audit_report.py, scripts/export_pdf.mjs, scripts/lib/harness.mjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

plugins/report/skills/report/SKILL.md · 658 lines

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.

Read the full file on GitHub · 658 lines

Changes

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.

  1. 1mo ago Changed · +2 lines · +23 tokens per session 1c6e0bf6d490
  2. 1mo ago First seen · 656 lines · 510 tokens per session scan A d7e8441e3d64

Subscribe to this mod's changes

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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