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/coco-research/coco/pm-advisorgit clone --depth 1 https://github.com/coco-research/cocoWhat 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 | $0.00063 | $0.01700 |
| Opus 5 | $0.00032 | $0.00850 |
| Sonnet 5 | $0.00013 | $0.00340 |
| Haiku 4.5 | $0.00006 | $0.00170 |
Grade A, and why
pm-advisor 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 204 lines · 63 tokens per session scan A 2ffb3972c360
pm-advisor is an agent published in the GitHub repository coco-research/coco (217 stars, last pushed 2d ago), with no licence file. It adds 63 tokens to every session and 1,700 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 2026-08-30.
Other agents, from other repositories
Deep Research
Execute a recursive, hypothesis-driven deep dive research workflow — orchestrating corpus scanning, web scouting, source fetching, sprint synthesis, bibliography enrichment, and academic paper generation. Instantiates the workflow defined in docs/guides/deep-research.md.
Research Scout
Conduct mandatory web searches and survey external authoritative sources for a given research topic. Catalogue raw findings in the session scratchpad — do not synthesize. Web sourcing is non-negotiable for all research sprints.
learn
Product retrospective agent. Runs after a release, after a measure agent anomaly flag, or at end of sprint. Maps findings to DORA AI capabilities and produces plan agent action items. Distinct from fawkes learn.md which handles platform incident postmortems.
product-manager
Owns the product strategy, roadmap, and business goals. Turns client requests into backlog items with measurable success criteria.
product-owner
Represents the customer, owns the work backlog, and decides what features get built first. The voice of the customer inside the team.
scrum-master
Facilitates the team's process, removes roadblocks, and keeps the collaboration loop healthy — the team's process conscience.