pstack is a collection of plain Agent Skills and engineering principles that guide AI-assisted software development across coding agents such as Claude Code, Codex, Pi, Cursor, and OpenCode. It is for developers who want reusable practices for tasks including testing, technical writing, debugging, review, and coordinating agent work. The catalogue entries are the project's skills, agents, and plugin.
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 backnotprop/pstack --skill reflectgit clone --depth 1 https://github.com/backnotprop/pstackWrote 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/backnotprop/pstack/reflect)<a href="https://agentmods.dev/skills/backnotprop/pstack/reflect"><img src="https://agentmods.dev/badge/skills/backnotprop/pstack/reflect/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/backnotprop/pstack/reflect"><img src="https://agentmods.dev/badge/skills/backnotprop/pstack/reflect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
- Socket pass
- Snyk pass
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 | $0.00036 | $0.01620 |
| Opus 5.5 | $0.00014 | $0.00648 |
| Sonnet 5.5 | $0.00007 | $0.00324 |
| Haiku 4.5 | $0.00004 | $0.00162 |
Grade A, and why
reflect 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 today.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- reflect — 100% identical, 72 lines differ
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect
Mine the current conversation for durable learnings, then route them into skill edits.
Invoke when the user says "reflect" or "/reflect". Skip when the conversation is trivial, off-topic, or already covered by an existing skill the parent followed correctly. One-offs are not learnings.
1. Locate the active transcript
Before fanning out, find this conversation's transcript in the active workspace's session directory. Do not glob across every project's directory. That crosses workspace boundaries and reads private chats from unrelated projects.
Where the transcript lives depends on your harness:
-
Cursor: the
agent-transcripts/directory the system prompt names.ls -t <agent-transcripts>/*.jsonl <agent-transcripts>/*/*.jsonl <agent-transcripts>/*/subagents/*.jsonl 2>/dev/null | head -10That covers the legacy flat (
<id>.jsonl), current nested (<id>/<id>.jsonl), and subagent (<parent>/subagents/<child>.jsonl) layouts. For each candidate, read the first JSONL line. Take the file whosemessage.content[0].textcontains the conversation's opening user prompt. -
Claude Code:
~/.claude/projects/<slug>/<session-id>.jsonl, where<slug>is the workspace path with every character that isn't a letter or digit turned into "-". -
Pi:
~/.pi/agent/sessions/--<slug>--/*.jsonl, where<slug>is the workspace path with the leading slash dropped and each "/" turned into "-". -
Codex:
~/.codex/sessions/YYYY/MM/DD/rollout-*.jsonl. Keep only files whose first line haspayload.cwdequal to the workspace path. -
Other harnesses: check the harness's session directory for this workspace.
Outside Cursor, take the newest candidates first and confirm one by finding the conversation's opening user prompt in its first user message. If none matches, pass a tight digest of the session instead.
2. Spawn three reviewers in parallel
One message, three Task calls, subagent_type: generalPurpose, agent mode (readonly: false). Reviewers need MCP access to look up the tickets, chat threads, and observability traces the transcript references, and readonly strips MCPs.
What ships with it
4 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.
- today Changed · +2 lines 4e04c17f5ee6
- 3d ago Changed 4ced5551d3ee
- 8d ago Changed ce537104c76f
- 11d ago Changed · -4 lines ac549993825f
- 15d ago Changed 241f3c5c8de2
- 19d ago Changed · +2 lines bbbba9bb0b7a
- 1mo ago Changed · +4 lines 68cc55d20e17
- 1mo ago First seen · 78 lines · 36 tokens per session scan A 5485347991eb
reflect is a skill published in the GitHub repository backnotprop/pstack (2,652 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 1,620 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
email-domain-security
Rigorous, defensible email-spoofability verdict and SPF supply-chain risk analysis computed from published DNS alone. Deepens the record-level SPF/DMARC/DKIM/BIMI/MTA-STS/DNSSEC fetch recipes in the offensive-osint arsenal (§16.14) with the reasoning that section doesn't do: a priority-ordered composite verdict for…
exposure-risk-quantification
FAIR-aligned exposure quantification: turns a pile of recon findings into a defensible 0-100 + A-F org risk score (Likelihood x Impact, three ownership-aware factors: exposure/threat/impact), an ownership + proof demotion cap so unproven or weakly-owned findings can't inflate the number, a $-denominated FAIR…
osint-methodology
Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments. Covers the 6-stage recon pipeline (seed → asset expansion → enrichment → exposure analysis → convergence → operator-armed active validation) with connector-resilience and stage-vs-gating discipline, asset-graph…
osint-autopilot
End-to-end external OSINT engagement autopilot. Run the FULL osint-methodology pipeline to completion in ONE go for an authorized domain — engagement folder, Stages 1-5 (seed, expansion, enrichment, exposure, convergence), multi-agent per-host content+JS fan-out, headline verification, auto-generated findings, and a…
awareness
AI situational awareness — internal threat detection for hallucination risk, scope creep, and context degradation. Maps Cooper color codes to reasoning states and OODA loop to real-time decisions. Use during any task where reasoning quality matters, when operating in unfamiliar territory, after detecting early warning…
collect-preserve-specimens
Collect and preserve insect specimens following museum-grade standards including legal compliance, collection methods, humane dispatch, dry pinning, wet preservation, labeling, storage, and curation. Covers permit requirements, protected species regulations, sweep nets, beating trays, pitfall traps, light traps…