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 OutlineDriven/outline-driven-development --skill compoundgit clone --depth 1 https://github.com/OutlineDriven/outline-driven-developmentWrote 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/outlinedriven/outline-driven-development/compound)<a href="https://agentmods.dev/skills/outlinedriven/outline-driven-development/compound"><img src="https://agentmods.dev/badge/skills/outlinedriven/outline-driven-development/compound/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/outlinedriven/outline-driven-development/compound"><img src="https://agentmods.dev/badge/skills/outlinedriven/outline-driven-development/compound.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00039 | $0.01446 |
| Opus 5 | $0.00019 | $0.00723 |
| Sonnet 5 | $0.00008 | $0.00289 |
| Haiku 4.5 | $0.00004 | $0.00145 |
Grade A, and why
compound 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge compound
Contract
| Field | Bound contract |
|---|---|
| Trigger | User explicitly asks to save, curate, or consolidate what was learned, or closes a meaningful knowledge-work session. |
| Authority | Reversible local: writes only docs/knowledge/{slug}.md records and repo-root CONCEPTS.md; rollback is version control. No remote mutation. Never silently deletes stale entries. |
| Side effect | Writes typed docs/knowledge/{slug}.md records and repo-root CONCEPTS.md; may update stale entries, but never silently delete them. |
| Done | At most three specific typed learnings or an honest none are proposed; duplicates and contradictions are checked; the user approves; saved records carry type, retrieval tags, confidence, date, source, Context, and Implication; confirmation names retrieval tags. |
Inputs
- Session transcript (required): the accumulated context of the current session, from which learnings are extracted.
- docs/knowledge/ directory (required): the existing knowledge record store; must exist and be readable.
Procedure
- Scan docs/knowledge/ for existing records. Load each record and extract its frontmatter tags, type, and source. Done when: every existing record is loaded with its frontmatter.
- Check for stale and conflicting entries: compare the extracted tags of each existing record against the candidate learnings. An entry is stale when its tags overlap with a new candidate and its content contradicts or supersedes the candidate. An entry conflicts when it shares tags but states the opposite. Flag each stale or conflicting entry by name with the overlap reason; do not delete or overwrite it. Done when: every stale or conflicting entry is flagged by name with its overlap reason.
- Propose at most three specific learnings drawn from the session transcript. Classify each with a type (e.g., pattern, caveat, reference, decision, concept, fix, lesson). Reject a fourth or fifth as scope creep; propose none if the session yielded no durable learning. Done when: at most three learnings are proposed with types, or an honest none is proposed.
- For each proposed learning: a. Check against the stale-knowledge inventory: if a duplicate or contradiction exists by shared tag, flag it explicitly rather than overwriting. Done when: the duplicate or contradiction is flagged or none exists. b. Assign retrieval tags, confidence level (high/medium/low), date (ISO 8601), and source context. Done when: tags, confidence, date, and source are assigned. c. Draft frontmatter per the Record schema below: type, tags, confidence, date, source, Context (what triggered the learning), Implication (what changes as a result). Done when: frontmatter is drafted with all seven fields.
- Present the proposed records to the user for approval. Include the stale/conflict flags so the user can decide whether to update, merge, or consolidate. Done when: the proposed records and flags are presented for approval.
- On user approval: write each approved record to docs/knowledge/{slug}.md using the drafted frontmatter and a prose body derived from the session. If a record updates or consolidates an existing entry, preserve the original entry's history line. Done when: every approved record is written with valid frontmatter and history preserved.
- Confirm completion by naming the retrieval tags for each saved record. Done when: the retrieval tags for each saved record are named in the confirmation.
What ships with it
2 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.
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 Changed · -30 tokens per session 900f3184441e
- 4d ago First seen · 81 lines · 69 tokens per session scan A b102c4900349
compound is a skill published in the GitHub repository OutlineDriven/outline-driven-development (52 stars, last pushed 3d ago), licensed Apache-2.0. It adds 39 tokens to every session and 1,446 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
autolearn
Use when a verified non-trivial fix lands or existing solution docs need refresh. Not for unverified fixes.
consolidate-to-one-home
Fold a scattered fact into a single home and point the rest at it. Use when the same rule, constant, or definition is restated in several files, or the user says "consolidate this", "make this the single source of truth", or "ssotize this". The consolidation is proposed and approved before any edit lands. To…
memory-clean
Audit memory directory for structural issues (orphans, dangling refs, duplicates, missing sections, oversized entries) and staleness against session-history transcripts; report-first, fix-on-confirmation. Use when the user says "audit memory", "memory hygiene", or "find stale/duplicate memories".
memory-update
Scan agent's session-history transcripts for save-worthy signals (corrections, preferences, decisions, references), propose and write auto-memory files with valid frontmatter and MEMORY.md entry. Use when the user says "save this to memory", "remember that", or "scan this session for memories".
memory-sanitize
Produce share-safe copies of memory files under /tmp with PII redacted (paths, emails, session IDs, dates) and credentials scanned (tokens, keys); never mutates originals. Use when the user says "sanitize memory for sharing", "redact memory PII", or "scan memory for credentials".
shellm
Reference for the shellm system — recursive LLM shell, identity management, memory, skills, trajectory, and all CLI tools. Use when working on shellm itself, debugging agent behavior, or understanding how the pieces fit together.