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 commands/dachhack/stathead/extract-pdf-featuresgit clone --depth 1 https://github.com/dachhack/statheadWhat 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 | $0.00019 | $0.01680 |
| Opus 5 | $0.00010 | $0.00840 |
| Sonnet 5 | $0.00004 | $0.00336 |
| Haiku 4.5 | $0.00002 | $0.00168 |
Grade A, and why
extract-pdf-features 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 yesterday.
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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are running the prospect-guide feature extraction pipeline. This command processes PDF text caches written by scripts/extract_pdf_features.py and turns each guide into structured per-player JSON.
Optional filename filter
The invocation may include an argument string: $ARGUMENTS
- If
$ARGUMENTSis empty / whitespace, process every PDF as normal. - Otherwise treat it as a case-insensitive substring. Only process text caches whose stem (the filename without
.text.txt) contains this substring. Skip everything else without touching their caches.
Examples: /extract-pdf-features rookie_scouting → only RSP guides. /extract-pdf-features 2025 → only 2025-dated PDFs. /extract-pdf-features → all of them.
Report to the user up-front: "Filter: (N of M PDFs match)" or "Filter: none (processing all N PDFs)".
Step 1: make sure text caches exist
Run python3 scripts/extract_pdf_features.py via Bash. It decrypts every PDF in pdfs/ and writes pdfs/.cache/*.text.txt. If it errors (missing deps, no PDFs, bad password), surface the error to the user and stop.
Step 1.5: load optional extraction context
If pdfs/.extraction-context.md exists and is non-empty, read it. Compute its short SHA-256 hash via Bash:
shasum -a 256 pdfs/.extraction-context.md | cut -c1-8
Hold onto two values for the rest of this run:
CONTEXT_TEXT: the file's contents (may be empty if the file doesn't exist)CONTEXT_HASH: the 8-char hash, or the literal stringnocontextif the file is missing/empty
The context lets the user inject domain-specific guidance (e.g. "Beast tiers go 1-5, not 1-3"; "treat 'Edge' positions as DL even if listed as LB") without editing this slash command. Including the hash in cache filenames (next step) ensures editing the file invalidates stale features.
Step 2: process each text cache
Use Glob to list pdfs/.cache/*.text.txt. For each text file:
- Compute the corresponding features path:
pdfs/.cache/<stem>.<CONTEXT_HASH>.features.jsonwhere<stem>is the filename without.text.txtand<CONTEXT_HASH>is from step 1.5. - If that features file already exists, skip this PDF (it's already been processed with the current context). Tell the user "skip : cached".
- Otherwise:
- Read the text file in full (it may be large; chunk via the Read tool's offset/limit if needed).
- Extract every distinct NFL draft prospect that gets a real write-up, ranking, or tier — see schema below. If
CONTEXT_TEXTis non-empty, treat its contents as additional rules that override or refine the schema rules below. - Write the result as a JSON array (NOT wrapped in
{"players": ...}) topdfs/.cache/<stem>.<CONTEXT_HASH>.features.jsonusing the Write tool.
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.
- yesterday First seen · 110 lines · 19 tokens per session scan A 38bcdb239505
extract-pdf-features is a command published in the GitHub repository dachhack/stathead (0 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 1,680 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.