Deliverable vs Instruction
Distinguish research deliverables from operating instructions to avoid planning against phantom requirements.
Installation
- Make sure Claude is on your device and in your terminal.
Skills load from
~/.claude/skills/when Claude Code starts up β so you need it on your machine first. If you don't have it yet, install it once with the command below, then runclaudein any terminal to verify.One-time setupnpm i -g @anthropic-ai/claude-codeAlready have it? Skip ahead.
- Paste into Claude Code or into your terminal.
This copies the whole skill folder into
~/.claude/skills/a-deliverable-is-not-an-instruction-tangxiangru/β the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.Faster alternative (instruction-only skills)
Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates β they won't be downloaded and the skill will fail when it tries to load them.
Quick install (SKILL.md only)Sign up to copy - Restart Claude Code.
Quit and reopen Claude Code (or any other agent that loads from
~/.claude/skills/). New skills are picked up on startup. - Just ask Claude.
Skills auto-activate when your request matches the skill's description β no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the βWhat this skill doesβ section above.
Prefer to read the source first? Open on GitHub.
When Claude uses it
Use at study design when listing the task's deliverables into report_plan.json, and again before writing when checking coverage. Covers how to tell a research deliverable from the harness's own operating instructions, why a padded list is worse than a short one, and what to write when a deliverable is genuinely out of reach.
What this skill does
The sheet you were handed is two documents. Only one of them is the task.
A task statement that arrives through a harness is rarely only the task. Around the research question sits a second document: how to operate, where to write files, what not to do, what counts as finished. Both are addressed to you, both are written in the imperative, and one of them is not a deliverable.
Enumerate the wrong one and the study is planned against phantoms.
The failure this prevents
Measured on a forty-task benchmark arm: the deliverable list drawn off the whole instruction sheet held 337 requirements; the research questions alone held 142. Fifty-eight per cent of what every run was planning against was the harness talking about itself, and the same five phantoms appeared in all forty runs:
Read & Understand β Study the related work and data to build domain context. Code & Execute β Implement the analysis, generate figures, and iterateβ¦ Analyze & Report β Interpret the results and produce a publication-quality report. Your primary goal is to β¦ produce a high-quality
report/report.md. Figures are mandatory β generate plots and save toreport/images/.
Every one is a true instruction. Not one is a finding. A plan that answers
"Read & Understand" with figure:2 has spent a figure slot on the fact that
reading happened.
The damage is not only wasted slots. A plan is checked for a covering answer to each listed deliverable, so a list of seventeen forces seventeen answers out of a study that has four results β and the entries that cannot be answered honestly get answered emptily. An empty entry fails the gate, the stage is sent back, and a stage sent back often enough is a stage that never happens.
The test
For each imperative sentence, ask: would the answer to this appear in the published paper?
| Sentence | In the paper? | Deliverable |
|---|---|---|
| "derive upper limits on the coupling strength" | yes, a number with an interval | yes |
| "compare against the incumbent method" | yes, a table | yes |
| "report the distribution across all seven datasets" | yes, a figure | yes |
| "study the related work to build context" | no β everyone does this | no |
| "save all figures as PNG" | no β that is file format | no |
| "install python packages as needed" | no | no |
| "produce a high-quality report" | no β that is the container | no |
The distinction is not importance. Saving figures as PNG matters enormously; it is just not a finding. Obey those instructions β they are how the work gets delivered β and keep them out of the list of things the work found.
A second cue: a deliverable is task-specific. If the sentence would read identically in a task about superconductors and a task about cloud seeding, it came from the template, not from the study.
Sizing the list
Three to six deliverables is the normal shape of a research task. If your list has grown past ten, you are almost certainly enumerating the template. Re-read it and cut, rather than inventing an answer for each.
Two entries that look like two deliverables are often one: "derive limits on masses and coupling strengths" is two, because either can be present without the other; "produce reproducible tables and figure-level evidence" is one result delivered two ways. Split on what could be missing independently, not on the conjunction.
When one is genuinely out of reach
Say so in the entry rather than leaving it blank or inventing a cover. Name the deliverable, say exactly what is missing β the data was not supplied, the reference implementation is not public, the compute is not there β and say what you did instead.
A blank entry is the worst of the three options. It fails the plan gate, which costs the stage; and if it survives, it claims coverage that does not exist. An honest "not reachable, because X" costs nothing and is a sentence the report can reuse in its limitations.
Before you leave study design
- Every entry names something that would appear in a published paper.
- No entry would read identically in a task from another field.
- No entry is blank.
- The count is in single digits, and each one is answered by a figure, a number, or a named section β not by "prose" used as a placeholder.
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