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.
git clone --depth 1 https://github.com/VandanaAjayDubey111/great-pmWrote 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/commands/vandanaajaydubey111/great-pm/pm-learn)<a href="https://agentmods.dev/commands/vandanaajaydubey111/great-pm/pm-learn"><img src="https://agentmods.dev/badge/commands/vandanaajaydubey111/great-pm/pm-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/commands/vandanaajaydubey111/great-pm/pm-learn"><img src="https://agentmods.dev/badge/commands/vandanaajaydubey111/great-pm/pm-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 | $0.00052 | $0.00853 |
| Opus 5.5 | $0.00021 | $0.00341 |
| Sonnet 5.5 | $0.00010 | $0.00171 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
pm-learn scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 18d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the great-pm /pm-learn command. Capture session learnings without
requiring a full /pm-save.
Parse arguments
--dry-run→ continuous-learner produces the lessons block but does NOT write it to disk. Print preview only.- (no flag) → write to
.great-pm/lessons.mdand promote ≥3-occurrence patterns to~/.great-pm/decisions.md.
Pre-flight
echo "cwd=$(pwd)"
mkdir -p .great-pm
ls .great-pm/lessons.md 2>/dev/null && echo "LESSONS_OK" || echo "FIRST_RUN"
ls .great-pm/.learn-pending 2>/dev/null && echo "PENDING_MARKER" || echo "NO_MARKER"
If PENDING_MARKER exists → this is the recommended path (SessionEnd hook
flagged something worth capturing).
Operating procedure
-
Step 0 — refine the user's query (transparent Mode B). Invoke
query-refiner-pmwith$ARGUMENTS. The refiner returns:You typed: <original> Refined to: <refined brief> What changed: <one line> Proceeding with refined. Reply "use original" to override.Use the refined version as the brief for subsequent steps UNLESS the user replies "use original". Log the refinement to
.great-pm/refinements/$(date +%Y-%m-%d).log. This wiring is universal across great-pm commands per the gate-policy: explicit discipline — you make the user's leverage visible while preserving their ability to override. -
Spawn
continuous-learneras a subagent with:- Mode:
auto-extract(no manual prompt). - Read the session history available to you.
--dry-runflag if set.
- Mode:
-
The agent identifies:
- Repeatable patterns (≥2 occurrences in this session).
- Decisions worth promoting (architectural, governance, naming).
- Cost outliers (long-running agent invocations, repeated tool failures).
-
Output format (appended to
.great-pm/lessons.md):## <YYYY-MM-DD HH:MM> session - pattern: <one-line> (count: N) - decision: <one-line> (rationale: <one-line>) - cost outlier: <one-line>
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.
- 18d ago First seen · 86 lines · 52 tokens per session scan A a9154fcd017f
pm-learn is a command published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 18d ago), licensed MIT. It adds 52 tokens to every session and 853 once invoked, about $0.0002 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 2026-09-21.
Other commands, from other repositories
scout-meta-review
Run an interactive Scout meta-review — a system-level audit that sits above the individual session types. Checks whether sessions are running, mistake audit is trending well, proposals are flowing, KB files are healthy, and data-source coverage is consistent across session types. Runs in the current conversation.
scout-work
Interactive work session — walks through today's actionable items one at a time, presents a recommended action with draft content, and executes each one only with explicit approval. Runs in the current conversation (not as a background session).
attune
AI-powered developer workflows with Socratic discovery.
bulk
Batch API processing with 50% cost savings.
migrate
Use when the user invokes /goal-flight migrate to preview and import existing markdown task lists into Goal Flight draft task-store items.
dashboard
Starts the live web dashboard for the current fellowship — quest/scout progress, gate approvals, and event history — in the background, and prints the URL.