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 TeamRetroHQ/teamretro-skills --skill teamretro-post-recommendationsgit clone --depth 1 https://github.com/TeamRetroHQ/teamretro-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/teamretrohq/teamretro-skills/teamretro-post-recommendations)<a href="https://agentmods.dev/skills/teamretrohq/teamretro-skills/teamretro-post-recommendations"><img src="https://agentmods.dev/badge/skills/teamretrohq/teamretro-skills/teamretro-post-recommendations/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/teamretrohq/teamretro-skills/teamretro-post-recommendations"><img src="https://agentmods.dev/badge/skills/teamretrohq/teamretro-skills/teamretro-post-recommendations.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 | $0.00112 | $0.01644 |
| Opus 5 | $0.00056 | $0.00822 |
| Sonnet 5 | $0.00022 | $0.00329 |
| Haiku 4.5 | $0.00011 | $0.00164 |
Grade A, and why
teamretro-post-recommendations 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 12d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Post AI Recommendations to TeamRetro
Beta. This skill depends on the TeamRetro MCP server and is still being hardened. The account-free practice (
ai-session-retro+ai-retro-brief) is the stable path; use this to post into TeamRetro and report anything that breaks.
The TeamRetro-native counterpart of ai-retro-brief. Where that skill prepares a summary the user brings to their retro however they run it, this one prepares the AI's recommendations and posts them into TeamRetro — so the AI's ideas land on the board next to everyone else's.
This is the AI-participates-in-the-retro flow. The AI's voice stays visible: every posted item is prefixed [AI retro] so a human reading the board knows a teammate's agent raised it, not a person. The human decides what gets posted and where; the AI never posts on its own.
Prerequisite — TeamRetro MCP connection
Requires an authenticated TeamRetro MCP connection (the server exposing create_parked_item, create_action, create_retrospective_idea, list_teams, list_retrospectives, get_retrospective). If those tools aren't present, say so plainly and stop:
No TeamRetro MCP connection is available in this session, so I can't post anything. You can still get the same analysis as a document with the
ai-retro-briefskill and bring it to your retro yourself.
Never fabricate a posting (no invented IDs, no "posted!" without a real tool response).
Step 1 — Prepare the recommendations
Synthesize recommendations from the AI session retro log exactly as ai-retro-brief does — read docs/ai-retros/entries/ (fewer than 3 entries: say so, recommend logging more sessions first, and only proceed if the user insists), rank recurring friction by frequency × cost, and derive the top recommended actions, each tied to specific dated entries. Follow that skill's rules: evidence-cited, unsoftened, fixed vocabulary as-is.
Save the analysis as a brief (docs/ai-retros/briefs/brief-YYYY-MM-DD.md, per ai-retro-brief's template, stamped teamretro-post-recommendations v0.2) and commit it — the brief is the record of what was recommended; the postings reference it. If the user points at an existing committed brief or entry instead, use its recommendations ("Top 3 recommended actions" / "Do this first") rather than re-deriving.
What ships with it
3 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.
- 12d ago First seen · 72 lines · 112 tokens per session scan A e9d196d2d87a
teamretro-post-recommendations is a skill published in the GitHub repository TeamRetroHQ/teamretro-skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 112 tokens to every session and 1,644 once invoked, about $0.0006 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.
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