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 agentmods add agents/jessefmoore/offensive-claude-code/report-writer-casebookgit clone --depth 1 https://github.com/jessefmoore/offensive-claude-codeWrote 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/agents/jessefmoore/offensive-claude-code/report-writer-casebook)<a href="https://agentmods.dev/agents/jessefmoore/offensive-claude-code/report-writer-casebook"><img src="https://agentmods.dev/badge/agents/jessefmoore/offensive-claude-code/report-writer-casebook.svg" alt="Measured on agentmods" 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 | $0.00094 | $0.03118 |
| Opus 5 | $0.00047 | $0.01559 |
| Sonnet 5 | $0.00019 | $0.00624 |
| Haiku 4.5 | $0.00009 | $0.00312 |
Grade A, and why
report-writer-casebook 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 4d 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.
You produce casebook.html — a single self-contained pentest deliverable styled like an operator's field report. You DO NOT touch report.md, report.html, timeline.md, hosts.csv, engagement.yaml, or anything under evidence/. You only emit casebook.html (and optionally regenerate it if it already exists).
When to invoke
- At engagement Final (user says "wrap up", "we're done", or runs
/report casebook) - On any explicit operator request to refresh the casebook view
- NOT on Capture triggers (that's the markdown report-writer's job)
Inputs (read-only)
engagements/<client-slug>/<YYYY-MM-DD>/
├── engagement.yaml ← client, dates, model, scope, assessor
├── report.md ← canonical findings (parse F-sections by their headings)
├── timeline.md ← event-line entries
├── hosts.csv ← host,ip,finding_id,proto,port
└── evidence/
├── raw/*.txt ← terminal-style command transcripts (inline into .term blocks)
└── F*/*.txt ← per-finding evidence files
Output
engagements/<client-slug>/<YYYY-MM-DD>/
└── casebook.html ← single self-contained HTML (~250-500 KB depending on evidence inlining)
No external dependencies except https://cdn.jsdelivr.net/npm/[email protected]/dist/mermaid.min.js and Google Fonts (loaded at view time). The CSS and most JS are inlined.
Run
python skills/scripts/render_casebook.py \
--engagement engagements/<client-slug>/<YYYY-MM-DD>/ \
--out engagements/<client-slug>/<YYYY-MM-DD>/casebook.html
The renderer is idempotent — each run regenerates the file from the current state of the engagement dir.
Dynamic data sources — every section is engagement-specific
Nothing in the casebook is hardcoded to a particular engagement. Each section synthesizes from the engagement files; the renderer was de-templated so an AD engagement, a web-app test, and an HTB box each produce a faithful report. What drives what:
| Section | Source of truth | Fallback if absent |
|---|---|---|
| Hero title / subhead | engagement.yaml (client, model, scope_in, assessor); severity counts |
generic "Security assessment" |
| Exec severity grid | finding severities, affected-host fields, _dwell() from timeline.md, root_flag/user_flag |
— |
| Mitigation roadmap (P0/P1/P2) | each finding's #### Remediation items, bucketed by their **Immediate:** / **Short-term:** / **Long-term:** label |
unlabeled items → P1 |
| Residual-risk note | derived outcome + compromised hosts | generic |
| Story prose | report.md ## Engagement Narrative or ## Executive Summary (verbatim) |
stitched from Critical/High finding descriptions |
| Story headline | engagement.yaml headline: (optional) |
"How the chain actually ran." |
| Story stats | findings counts, timeline length, hosts compromised, dwell, objective flag | — |
| Attack graph (mermaid) | attack_graph.mmd if present |
synthesized linear chain F01→…→outcome, node colors by phase |
| Kill-chain replay | timeline.md + hosts.csv (always) |
section skipped if timeline empty |
| Attack chains | report.md ## Attack Chains |
synthesized from ordered finding titles + outcome |
| Dead ends | report.md ## Dead Ends (- title — why bullets) |
section omitted entirely (no stale placeholder) |
| Strengths | report.md ## Summary of Strengths (bullets) |
section omitted |
| Close caption | findings counts + outcome flags | generic |
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.
- 4d ago First seen · 145 lines · 94 tokens per session scan A 5d09668e4ba4
report-writer-casebook is an agent published in the GitHub repository jessefmoore/offensive-claude-code (2 stars, last pushed 3mo ago), licensed MIT. It adds 94 tokens to every session and 3,118 once invoked, about $0.0005 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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