claude-code-my-workflow is a forkable setup for using Claude Code to produce and review academic papers, slides, data analyses, and replication packages. Researchers use its agents, skills, rules, hooks, and quality checks to coordinate these tasks and verify their results. The catalogue entries define the reusable workflow components for Claude Code.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/pedrohcgs/claude-code-my-workflownpx agentmods add skills/pedrohcgs/claude-code-my-workflow/promote-memoryWrote 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/pedrohcgs/claude-code-my-workflow/promote-memory)<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/promote-memory"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/promote-memory/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/pedrohcgs/claude-code-my-workflow/promote-memory"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/promote-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Output Handling · line 23 Output size or generation rate is not bounded. Unbounded output enables denial-of-service through resource exhaustion, log flooding, or context-window stuffing.Fix: Set explicit limits on output length, generation count, and rate. Use max_tokens and truncation to prevent unbounded output.
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 | $0.00000 | $0.02871 |
| Opus 5.5 | $0.00000 | $0.01148 |
| Sonnet 5.5 | $0.00000 | $0.00574 |
| Haiku 4.5 | $0.00000 | $0.00287 |
Grade A, and why
promote-memory 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 25d 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/promote-memory — five-critic council for memory promotion
The template's meta-governance.md rule splits memory into two tiers:
MEMORY.md(committed, ≤ 200 lines) — generic learnings that help all forkers.- native auto memory (
~/.claude/projects/<project>/memory/— machine-local, typeduser/feedback/project/reference, no size cap on topic files) — machine-specific and user-specific learnings.
The rule says generic patterns should sync via git; personal patterns stay local. What it doesn't say is who decides which is which. /promote-memory operationalizes the call: spawn five critics in parallel, each reviewing the candidate [LEARN] entries on a single dimension, and promote on majority vote (3+ of 5).
When to use
- Monthly memory maintenance. Personal-memory accumulates faster than MEMORY.md; the council periodically harvests the genuinely generic learnings.
- Before sharing a fork. Someone is about to clone your template — what should they inherit?
- After a large project ships. Lessons from a paper or a course cycle deserve curation before the next project starts adding noise.
- Not on a schedule. This skill is user-invoked (
disable-model-invocation), so a scheduled task cannot fire it, and its candidates live in machine-local auto memory that a cloud routine cannot see. Set yourself a monthly reminder and run it in a local session; every promotion waits for your approval anyway.
When NOT to use
- For a single fresh
[LEARN]after a single correction. Just let auto memory record it; let it sit until the next council runs. - For deleting stale entries. Edit MEMORY.md by hand, per
meta-governance.md(dated addendum, or a merge to hold the cap)./promote-memorynever deletes — though near the cap it proposes a demotion (Step 4). - For project-specific context. That belongs in CLAUDE.md or session logs, not in either memory tier.
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.
- 25d ago Changed · +2 lines a0c58dc2ac37
- 1mo ago First seen · 178 lines · 0 tokens per session scan A f51b2fe7ae29
promote-memory is a skill published in the GitHub repository pedrohcgs/claude-code-my-workflow (1,668 stars, last pushed 25d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,871 tokens. 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
contribute
Session knowledge contribution to the shared brain.
brain-sync
Knowledge sharing lifecycle management.
r-econometrics
Run IV, DiD, and RDD analyses in R with proper diagnostics.
paper-workflow
A staged workflow for producing an evidence-based economics or social-science research paper, from choosing a topic through submission. It coordinates existing tools and analysis options, including Python, Stata, and R.
econ-management-paper-polish
A writing and editing guide for papers in economics, management, finance, accounting, marketing, information systems, public administration, and related fields.
analysis-state-management
Use whenever an analysis phase, decision log, artifact registry, run record, or subagent handoff needs to be created, updated, resumed, or compacted. Replaces the old habit of growing and rereading one long plan file with a small docs/analysis/ YAML index and task-specific records. Use at phase boundaries…