whatdidimiss
Skill1 starsChanged 5 months agoMITView on GitHub
After a chunk of work is done, surfaces what the model noticed but was never asked to do — adjacent tasks, unraised risks, natural follow-ups, and contradictions with the original plan. Opt-in only; the model reviews the work it just completed and the context it gathered, then returns a tight ranked list, each item with a one-line "worth doing? (y/n)". Triggers on "/whatdidimiss", "what did I miss", "what did you uncover", "anything else worth doing", "did we miss anything", "what else came up". NOT a fresh audit and NOT always-on — it only reports genuine findings from the work just done, and says "nothing notable" when there are none.
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 32
Keep each item to a single line.
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
What Did I Miss
Review the work you JUST completed in this session and surface the things you noticed but were never explicitly asked to handle. This is the "while you were in there, what else did you see?" pass.
Why this exists
Doing a task generates side-knowledge — an adjacent bug, a risky edge case, an obvious next step, a fact that quietly contradicts the original plan. That knowledge normally evaporates when the turn ends. This skill makes you spend it before it's gone. It is opt-in (Lucas invokes it deliberately) precisely so it never nags — do not propose wiring it into a hook, Stop event, or settings.json.
What to do
- Look back at the actual work, not the whole repo. Ground this in what you did and read THIS session: files touched, commands run, errors seen, code/context examined, the original ask. Do not go spelunking for new problems — this is reflection on work done, not a fresh audit.
- Surface only genuine findings, sorted best-first, across these four buckets (omit any bucket that's empty — don't pad to fill it):
- (a) Adjacent — things you noticed but didn't do (a nearby bug, a stale comment, a sibling file with the same issue).
- (b) Risks / edge cases — failure modes or edge cases you saw but didn't raise (empty input, race, missing auth check, unhandled error path).
- (c) Follow-ups — the natural next step the work sets up (a test to add, a migration to run, a doc to update, a caller to update).
- (d) Contradictions — anything you found that conflicts with the original plan, a stated assumption, or memory/ground-truth (the request assumed X but the code does Y).
- Format — one line per item, ranked, with a label and a verdict:
Keep each item to a single line. No sub-bullets, no code blocks, no re-explaining what you built. Total output stays short — aim for the few that matter, not an exhaustive list. If you have more than ~6, keep the top 6 and drop the rest.1. [a] Three other call-sites use the old signature — they'll break at runtime. — worth doing? y 2. [b] New endpoint has no rate-limit; trivial to abuse. — worth doing? y 3. [c] CHANGELOG not updated for this change. — worth doing? y 4. [d] Ask assumed Qdrant, but this path writes to pgvector. — worth doing? y/n (flag — confirm intent) - The hard rule: do not manufacture work. If nothing genuinely surfaced, reply exactly
Nothing notable.and stop. A short honest answer beats a padded one — inventing busywork is the failure mode this skill exists to avoid. Findings must trace to something you actually saw, not generic best-practice you could append to any task. - End with the offer, one line: "Want me to take any of these? (numbers, or n)" — then wait. Don't start on them unprompted.
Failure modes
- Invoked after a trivial or no-op turn (a one-line answer, a single read) → almost always
Nothing notable.Don't strain to produce items. - Tempted to re-audit the codebase → out of scope. Only report what THIS session surfaced. For a real audit use
/critique,/grill, or/pp2p. - Padding → the moment an item is "you could also add tests/docs/types in general" with no specific trigger from the work, cut it.
Not this skill
- Adversarial review of code you wrote →
/critiqueor/grill. - Polish / hardening passes →
/pp2p,/pp3p. - End-of-session tech-debt scan across the project →
techdebt.
In this repository54 other files it gives its agents.
- AGENTS.mdAGENTS.md
- skills/deep-recon/agents.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- adversarial-researchSkill
- algorithmic-artSkill
- api-integrationSkill
- breakdownSkill
- c4-architectSkill
- canvas-designSkill
- coding-agentsSkill
- compound-engineeringSkill
- context-budgetSkill
- counterfactualSkill
- critiqueSkill
- d3js-visualizationSkill
- data-analyst-agentSkill
- database-migrationsSkill
- deep-reconSkill
- design-tasteSkill
- doc-coauthoringSkill
- e2b-sandboxSkill
- ecosystem-auditSkill
- excalidraw-diagramSkill
- executing-plansSkill
- frontend-designSkill
- fullstack-agentSkill
- grillSkill
- kickoffSkill
- mcp-builderSkill
- model-migrationSkill
- naming-the-machineSkill
- full-output-enforcementSkill
- package-researchSkill
- pdf-generationSkill
- pp2pSkill
- pp3pSkill
- prompt-engineerSkill
- prompt-groundingSkill
- redesignSkill
- requesting-code-reviewSkill
- reverse-engineer-to-learnSkill
- review-changesSkill
- scratchpadSkill
- shipSkill
- slack-gif-creatorSkill
- sprint-architectSkill
- techdebtSkill
- test-and-fixSkill
- threat-modelSkill
- verification-before-completionSkill
- visual-verdictSkill
- webapp-testingSkill
- web-asset-generatorSkill
- writing-plansSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

