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/doneyli/claude-code-plugins/ingestgit clone --depth 1 https://github.com/doneyli/claude-code-pluginsWhat 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 | $0.00065 | $0.01937 |
| Opus 5 | $0.00032 | $0.00968 |
| Sonnet 5 | $0.00013 | $0.00387 |
| Haiku 4.5 | $0.00006 | $0.00194 |
Grade A, and why
ingest 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 2d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compile raw sources into the wiki following the workflow in ${CLAUDE_PLUGIN_ROOT}/skills/wiki-compiler/SKILL.md.
Step 0 — Auto-convert non-markdown files
Before ingesting, check if any target files are non-markdown. Claude Code can natively read these formats — use the Read tool to read them, extract the content, and write a .md conversion into raw/.
Natively convertible formats (no external tools needed):
| Extension | How to convert |
|---|---|
.pdf |
Read the PDF (up to 20 pages). Write a markdown version preserving headings, lists, tables, and key content. |
.png, .jpg, .jpeg |
Read the image (multimodal). Write a markdown file with a description/caption and any visible text (OCR). |
.csv, .tsv |
Read the file. Write a markdown file with the data as a markdown table (first 100 rows) and column summary stats. |
.json |
Read the file. Write a markdown file: arrays-of-objects as tables, nested objects as nested headings. |
.html, .htm |
Read the file. Extract the article body, strip nav/footer/ads, write clean markdown. |
.ics |
Read the file. Write a chronological event listing (date, title, location, description). |
.eml |
Read the file. Write markdown with Subject/From/To/Date in frontmatter and body as content. |
.txt |
Read the file. Wrap in frontmatter, keep body verbatim. |
Not natively convertible (warn the user):
.docx→ suggest:pandoc -f docx -t gfm file.docx > raw/file.md.pptx→ suggest: installpython-pptxor use the CLI'sllm-wiki import.m4a,.mp3,.wav→ suggest: installwhisper.cppor use the CLI.mbox→ suggest: use the CLI'sllm-wiki import
Conversion procedure for each non-md file:
- Read the file using the Read tool (Claude natively handles PDF, images, CSV, etc.)
- Write a converted markdown file to
raw/<slug>.mdwhere slug = kebab-cased original filename. Include this frontmatter:--- source_type: pdf | image | csv | json | html | ics | eml | txt source_path: <original file path> imported_at: YYYY-MM-DD original_title: <extracted title or filename> conversion_tool: claude-native conversion_confidence: high | medium | low ---highfor text-based (CSV, JSON, HTML, EML, ICS, TXT)mediumfor PDF (layout may lose structure)lowfor images (OCR/description is approximate)
- Move the original file to
raw/_originals/<original-name>(create_originals/if needed) - Proceed to ingest the new
.mdfile
Important: the PreToolUse hook blocks writes to raw/. For this conversion step ONLY, write the .md files using the Write tool — the hook allows writes to raw/*.md and raw/_originals/. It blocks edits to existing raw files, not creation of new converted markdown.
Wait — the hook blocks ALL writes to raw/. For the conversion step to work, you need to write the converted .md into raw/. Two options:
- Write the converted content to a temp location, then tell the user to move it into
raw/. Clunky. - Better: write the converted
.mddirectly intowiki/_converted/as an intermediate staging area, then ingest from there.
Actually simplest: just ingest the non-md file directly — read it via the Read tool, and proceed with the normal ingest workflow (steps 1-7 below) using the file's content as if it were markdown. No intermediate .md file needed. The source file stays in raw/ untouched, and wiki pages cite it as raw/<original-filename> in their sources: frontmatter.
Use this approach: When you encounter a non-md file, read it natively, hold its content in memory, and proceed with the ingest workflow below. The wiki page's sources: field cites the original file (e.g., raw/report.pdf). No conversion step writes to disk.
For unsupported formats, print a warning and skip: "Skipping raw/<file> — format not supported natively. Use the CLI's llm-wiki import to convert."
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.
- 2d ago First seen · 119 lines · 65 tokens per session scan A 079c86345661
ingest is a command published in the GitHub repository doneyli/claude-code-plugins (4 stars, last pushed 4mo ago), licensed MIT. It adds 65 tokens to every session and 1,937 once invoked, about $0.0003 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.