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-crystallize)<a href="https://agentmods.dev/commands/vandanaajaydubey111/great-pm/pm-crystallize"><img src="https://agentmods.dev/badge/commands/vandanaajaydubey111/great-pm/pm-crystallize/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-crystallize"><img src="https://agentmods.dev/badge/commands/vandanaajaydubey111/great-pm/pm-crystallize.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 | $0.00048 | $0.00801 |
| Opus 5 | $0.00024 | $0.00400 |
| Sonnet 5 | $0.00010 | $0.00160 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
pm-crystallize scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the great-pm /pm-crystallize command. Promote a battle-tested lesson to cross-project memory.
Governance reminder
You PROPOSE; the human DECIDES. Never write to ~/.great-pm/decisions.md without explicit human approval in this turn.
Operating procedure
-
Step 0 — refine the user's query (transparent Mode B). Invoke
query-refiner-pmwith$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. -
Identify candidates:
- If
$ARGUMENTSempty orauto→ spawncontinuous-learnerto scan all of.great-pm/lessons.mdfor entries withHits: 3(or higher) ANDConfidence: high. - If
$ARGUMENTSis a lesson slug → locate that specific entry.
- If
-
For each candidate, build a proposal:
Promotion proposal — <slug> Source lessons (N entries in .great-pm/lessons.md): - <YYYY-MM-DD>: <title> - ... Recommended cross-project rule (one line): <rule> Applies to archetypes: <list — e.g. b2b-saas, consumer-app, all> Status: PENDING HUMAN APPROVAL -
Present the proposals to the human and STOP. Wait for an explicit "yes promote " or "promote all" before writing.
-
On approval for a specific slug:
- Append the proposal text +
Crystallized: <YYYY-MM-DD>to~/.great-pm/decisions.md. - Mark the source entries in
.great-pm/lessons.mdwithPromoted: yes(Edit; not rewrite).
- Append the proposal text +
Output shape
Crystallization candidates (Hits ≥3, Confidence: high)
[1] <slug>
Sources: <N> lessons (<dates>)
Rule: <one-line>
Applies-to: <archetype(s)>
[2] <slug>
...
To promote: say "promote <slug>" or "promote all".
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.
- 11d ago First seen · 85 lines · 48 tokens per session scan A ea606fbf6137
pm-crystallize is a command published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 801 once invoked, about $0.0002 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
learn
Manually run the continuous-learner. Extract patterns from this session and write to .greatcto/lessons.md. Use when SessionEnd hook missed something or you want to capture a lesson mid-session.
ccr
CCR (Compressed Context with Retrieval) — recall the full original of context that greatcto compressed/filtered out, by its short id. The retrieval half of the compression layer.
compact-prep
Ask the agent to prepare for conversation compaction by updating any relevant state and providing guidance for the compaction agent and to kick off the session there after.
CLAUDE
Multi-agent collaboration platform for persistent, proactive AI agents across rooms, workspaces, skills, and external services.
build-corpus
Use when the user invokes /goal-flight build-corpus to build or refresh the private Goal Flight dispatch-context corpus.
rekindle
Recover a fellowship after a session crash. Scans worktrees and quest state, presents a recovery dashboard, and re-spawns Gandalf with recovered quest context. Use when returning to a crashed or expired fellowship session.