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/ololand-ai/ololand-plugins/sourcegit clone --depth 1 https://github.com/ololand-ai/ololand-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 | $0.00019 | $0.01375 |
| Opus 5 | $0.00010 | $0.00687 |
| Sonnet 5 | $0.00004 | $0.00275 |
| Haiku 4.5 | $0.00002 | $0.00137 |
Grade A, and why
source 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deal Sourcing
End-to-end sourcing pipeline that compounds: every selected company persists in the tenant-owned sourcing ledger before contact enrichment or outreach begins.
Usage
/source <criteria>
If no criteria provided, ask the user for sector, geography, size band (revenue or EBITDA), and any negative filters.
Execution
First classify the request. If it asks to create, list, update, deactivate, or
review matches for a standing thesis, route directly to the thesis operations
below. Do not load deal-sourcing or run the one-off discovery, watchlist,
candidate-persistence, contact-import, or outreach pipeline for a thesis
request. Otherwise, load the deal-sourcing skill and run this pipeline:
- Discover targets — call
mcp__ololand__search_company_discoverywith:query: the user's sector/product/market thesismode: "discover"company_scope: "private"unless the user explicitly includes public targetsfilters: geography, industry/sector, ownership, size, and negative filterslimit: 25Usemcp__ololand__natural_language_company_searchonly when the criteria cannot be represented as structured filters.
- Create the mandate — call
mcp__ololand__create_watchlistwith the user's original criteria and a descriptive name. Reuse an existing watchlist only when its criteria are materially identical. - Persist candidates immediately — pass the selected discovery result
objects unchanged to
mcp__ololand__save_sourcing_candidates, withwatchlist_idset to the ID returned or reused in step 2 andcandidatesset to the selected result objects. This captures the source snapshot, evidence references, match rationale, and candidate stage before any third-party enrichment. Repeated calls are idempotent. - Capture supported contacts — when a discovery result includes an
executive with a real email, phone number, or LinkedIn URL, select at most
one founder/CEO/CFO and pass that returned evidence to
mcp__ololand__openclaw_import_contactswithsource_system: "company_discovery". OloLand performs tenant-scoped identity dedupe and central do-not-contact checks. If discovery did not return usable contact evidence, leave the candidate shortlisted and report the gap. - Link the relationship — call
mcp__ololand__update_sourcing_candidatewith the step-2watchlist_id, saved candidatematch_id, resultingoutreach_contact_id, andsourcing_stage: "enriched". - Find a hook — prefer the candidate's returned
search_snippets,signal_summary, andma_signal_summary. If those are insufficient, perform a current public-web search for a funding round, hiring spike, leadership change, product launch, or expansion. Do not call deal-scoped research tools before a Deal exists. - Prepare outreach copy — write a 60-90 word proposed email in the command
response using the specific hook. This is reviewable copy, not a Gmail or
OloLand outreach draft, and it is never sent. Keep the candidate at
enriched(orshortlistedwhen no contact was captured).
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 · 116 lines · 19 tokens per session scan A 4ac9b315964c
source is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 4d ago), licensed Apache-2.0. It adds 19 tokens to every session and 1,375 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.