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 AntonioBlago/peec-ai-skills --skill peec-learngit clone --depth 1 https://github.com/AntonioBlago/peec-ai-skillsWrote 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/antonioblago/peec-ai-skills/peec-learn)<a href="https://agentmods.dev/skills/antonioblago/peec-ai-skills/peec-learn"><img src="https://agentmods.dev/badge/skills/antonioblago/peec-ai-skills/peec-learn/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/antonioblago/peec-ai-skills/peec-learn"><img src="https://agentmods.dev/badge/skills/antonioblago/peec-ai-skills/peec-learn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00119 | $0.02595 |
| Opus 5.5 | $0.00048 | $0.01038 |
| Sonnet 5.5 | $0.00024 | $0.00519 |
| Haiku 4.5 | $0.00012 | $0.00260 |
Grade A, and why
peec-learn 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 2mo 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SkillMind Learner
Role
Turn project-local Peec outputs into cross-project patterns. Each run does two things:
- Write — extract 1–3 patterns from a just-produced artifact (decision, brief, zone map, outreach log, learnings.json) and store them in SkillMind with tags so they can be retrieved later.
- Read — on request, recall patterns matching a project / skill / gap type and hand them back as priors for the next orchestrator cycle.
This is the memory layer beneath peec-report: that skill persists learnings for the project, this skill promotes them across projects.
Input
For write mode:
project_id— Peec project the artifact came fromsource_skill— which skill produced the artifact (peec-agent,peec-cluster,peec-outreach,peec-content-intel,peec-report)artifact_pathorartifact_content— the file or inline content to extract from- optional
max_patterns— default 3
For read mode:
query— what the caller wants to recall (e.g."editorial outreach DACH high citation rate")- optional
project_id— narrow to patterns originally written for this project - optional
source_skill— narrow to patterns originally written by this skill - optional
k— default 5
Output
Write mode: JSON list of {pattern_id, title, tags, summary} for each persisted pattern, plus a one-line confirmation ("added 3 patterns · skipped 1 dupe").
Read mode: ranked list of {pattern_id, title, summary, provenance: {project_id, source_skill, date}, score}. Empty list is a valid result — say so plainly.
Neither mode produces dashboards.
When to use
Write:
- Right after
peec-reportemitslearnings.json - After a
peec-outreachbatch closes (week-end ritual) - After
peec-clusterships a zone map (zones become reusable taxonomy patterns) - After
peec-agentlogs a decision whose 4-week metric came in (attribution is known) - After
peec-content-intelships a brief that later won its prompt (write the retrospective pattern, not the brief itself)
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.
- 2mo ago First seen · 251 lines · 119 tokens per session scan A 52ef16584d41
peec-learn is a skill published in the GitHub repository AntonioBlago/peec-ai-skills (10 stars, last pushed 6mo ago), licensed MIT. It adds 119 tokens to every session and 2,595 once invoked, about $0.0005 per session on Opus 5.5. 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.
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